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The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC. A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves. Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α. [ABSTRACT FROM AUTHOR]

Abstract Background Blood–brain barrier (BBB) dysfunction has been viewed as a potential underlying mechanism of neurodegenerative disorders, possibly involved in the pathogenesis and progression of Alzheimer’s disease (AD). However, a relation between BBB dysfunction and dementia with Lewy bodies (DLB) has yet to be systematically investigated. Given the overlapping clinical features and neuropathology of AD and DLB, we sought to evaluate BBB permeability in the context of DLB and determine its association with plasma amyloid-β (Aβ) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods For this prospective study, we examined healthy controls (n = 24, HC group) and patients diagnosed with AD (n = 29) or DLB (n = 20) between December 2020 and April 2022. Based on DCE-MRI studies, mean rates of contrast agent transfer from intra- to extravascular spaces (K trans) were calculated within regions of interest. Spearman’s correlation and multivariate linear regression were applied to analyze associations between K trans and specific clinical characteristics. Results In members of the DLB (vs HC) group, K trans values of cerebral cortex (p = 0.024), parietal lobe (p = 0.007), and occipital lobe (p = 0.014) were significantly higher; and K trans values of cerebral cortex (p = 0.041) and occipital lobe (p = 0.018) in the DLB group were significantly increased, relative to those of the AD group. All participants also showed increased K trans values of parietal ( $$\upbeta$$ β = 0.391; p = 0.001) and occipital ( $$\upbeta$$ β = 0.357; p = 0.002) lobes that were significantly associated with higher scores of the Clinical Dementia Rating, once adjusted for age and sex. Similarly, increased K trans values of cerebral cortex ( $$\upbeta$$ β = 0.285; p = 0.015), frontal lobe ( $$\upbeta$$ β = 0.237; p = 0.043), and parietal lobe ( $$\upbeta$$ β = 0.265; p = 0.024) were significantly linked to higher plasma Aβ1-42/Aβ1-40 ratios, after above adjustments. Conclusion BBB leakage is a common feature of DLB and possibly is even more severe than in the setting of AD for certain regions of the brain. BBB leakage appears to correlate with plasma Aβ1-42/Aβ1-40 ratio and dementia severity.

The glial lymphatic system is a recently discovered anatomical structure in the field of neuroscience, with functions such as regulation of interstitial fluid movement, waste removal and potentially brain immunity, playing an important role in physiology and pathology of the central nervous system. With the development of imaging technology, more and more magnetic resonance imaging (MRI) techniques have been applied to the study of the glial lymphatic system of the human brain. Currently, the commonly used imaging techniques include dynamic contrast-enhanced MRI, diffusion tensor image analysis along the perivascular space, and novel multimodal ultra-fast magnetic resonance techniques, etc. This article summarizes and reviews the application of these techniques in the brain glymphatic system, in order to provide a reference for the imaging study of the glymphatic system.

Abstract Objectives To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). Methods A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). Results The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. Conclusion Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. Clinical relevance statement The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. Key Points We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI. Graphical Abstract

To investigate the association of quantitative parameter (apparent diffusion coefficient [ADC]) from diffusion-weighted imaging (DWI) and various quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with Ki-67 proliferation index (PI) in cervical carcinoma (CC). A total of 102 individuals with CC who received 3.0 T MRI examination (DWI and DCE MRI) between October 2016 and December 2022 were enrolled in our investigation. Two radiologists separately assessed the ADC parameter and various quantitative and semiquantitative parameters including (volume transfer constant [ K trans ], rate constant [ k ep ], extravascular extracellular space volume fraction [ v e ], volume fraction of plasma [ v p ], time to peak [TTP], maximum concentration [MaxCon], maximal slope [MaxSlope] and area under curve [AUC]) for each tumor. Their association with Ki-67 PI was analyzed by Spearman association analysis. The discrepancy between low-proliferation and high-proliferation groups was subsequently analyzed. The receiver operating characteristic (ROC) curve analysis utilized to identify optimal cut-off points for significant parameters. Both ADC (ρ = − 0.457, p < 0.001) and K trans (ρ = − 0.467, p < 0.001) indicated a strong negative association with Ki-67 PI. Ki-67 PI showed positive correlations with TTP, MaxCon, MaxSlope and AUC (ρ = 0.202, 0.231, 0.309, 0.235, respectively; all p values<0.05). Compared with the low-proliferation group, high-Ki-67 group presented a significantly lower ADC (0.869 ± 0.125 × 10−3 mm2/s vs. 1.149 ± 0.318 × 10−3 mm2/s; p < 0.001) and K trans (1.314 ± 1.162 min−1vs. 0.391 ± 0.390 min−1; p < 0.001), also significantly higher MaxCon values (0.756 ± 0.959 vs. 0.422 ± 0.341; p < 0.05) and AUC values (2.373 ± 3.012 vs. 1.273 ± 1.000; p < 0.05). The cut-offs of ADC, K trans , MaxCon and AUC for discrimating low- and high-Ki-67 groups were 0.920 × 10−3 mm2/s, 0.304 min−1, 0.209 and 1.918, respectively. ADC, K trans, TTP, MaxCon, MaxSlope and AUC are associated with Ki-67 PI. ADC and K trans exhibited high performance to discriminate low and high Ki-67 status of CC. • ADC and K trans were significantly and inversely correlated with Ki-67 proliferation index in cervical cancer. • In the high-Ki67 PI group, ADC and K trans were significantly lower than those of the low-Ki67 PI group. • DWI and DCE parameters could be used to predict the Ki-67 PI in cervical cancer. [ABSTRACT FROM AUTHOR]

To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839–0.898) and 0.847 (95% CI: 0.787–0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC. [ABSTRACT FROM AUTHOR]

Purpose: A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and methods: This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7 years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5 min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. Results: 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland–Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were −9.46 mm3 and −50.87 mm3, respectively. Conclusion: Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients. [ABSTRACT FROM AUTHOR]

Background: Blood–brain barrier (BBB) dysfunction has been viewed as a potential underlying mechanism of neurodegenerative disorders, possibly involved in the pathogenesis and progression of Alzheimer's disease (AD). However, a relation between BBB dysfunction and dementia with Lewy bodies (DLB) has yet to be systematically investigated. Given the overlapping clinical features and neuropathology of AD and DLB, we sought to evaluate BBB permeability in the context of DLB and determine its association with plasma amyloid-β (Aβ) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: For this prospective study, we examined healthy controls (n = 24, HC group) and patients diagnosed with AD (n = 29) or DLB (n = 20) between December 2020 and April 2022. Based on DCE-MRI studies, mean rates of contrast agent transfer from intra- to extravascular spaces (Ktrans) were calculated within regions of interest. Spearman's correlation and multivariate linear regression were applied to analyze associations between Ktrans and specific clinical characteristics. Results: In members of the DLB (vs HC) group, Ktrans values of cerebral cortex (p = 0.024), parietal lobe (p = 0.007), and occipital lobe (p = 0.014) were significantly higher; and Ktrans values of cerebral cortex (p = 0.041) and occipital lobe (p = 0.018) in the DLB group were significantly increased, relative to those of the AD group. All participants also showed increased Ktrans values of parietal (β = 0.391; p = 0.001) and occipital (β = 0.357; p = 0.002) lobes that were significantly associated with higher scores of the Clinical Dementia Rating, once adjusted for age and sex. Similarly, increased Ktrans values of cerebral cortex (β = 0.285; p = 0.015), frontal lobe (β = 0.237; p = 0.043), and parietal lobe (β = 0.265; p = 0.024) were significantly linked to higher plasma Aβ1-42/Aβ1-40 ratios, after above adjustments. Conclusion: BBB leakage is a common feature of DLB and possibly is even more severe than in the setting of AD for certain regions of the brain. BBB leakage appears to correlate with plasma Aβ1-42/Aβ1-40 ratio and dementia severity. [ABSTRACT FROM AUTHOR]

Background: Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung. Purpose: To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference. Material and Methods: In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis. Results: The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05). Conclusion: Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions. [ABSTRACT FROM AUTHOR]

Simple Summary: The identification of gliomas and the differentiation between different types is essential to evaluate patients' prognosis and guide optimal clinical management. The ideal multiparametric magnetic resonance imaging (MRI) protocol for the assessment of gliomas is a current topic of research. This study aimed to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating molecular subtypes of adult-type diffuse gliomas. The results showed that a combined evaluation of DCE-MRI and DKI parameters reveals the best prediction of high-grade vs. low-grade gliomas, IDH1/2 wildtype vs. mutated gliomas, and astrocytomas/glioblastomas vs. oligodendrogliomas. The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2–4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900–1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702–0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700–0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification. [ABSTRACT FROM AUTHOR]

Objectives: To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). Methods: A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). Results: The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. Conclusion: Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. Clinical relevance statement: The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. Key Points: We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI. [ABSTRACT FROM AUTHOR]

Objective: To investigate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with serum squamous cell carcinoma antigen (SCCA), tumor specific growth factor (TSGF) and epididymis protein 4 (HE4) in lymph node metastasis (LNM) of early cervical cancer. Methods: 112 patients with early cervical cancer were divided into LNM group (27 cases) and non LNM group (85 cases) according to the results of postoperative pathological examination. The general data, DCE-MRI quantitative parameters [rate constant (Kep), volume transfer constant (Ktrans), extravascular extracellular volume fraction (Ve)], serum SCCA, TSGF and HE4 levels were compared between two groups. The predictive value of DCE-MRI quantitative parameters com- bined with serum SCCA, TSGF and HE4 for LNM in patients with early cervical cancer were analyzed by receiver operating characteristic (ROC) curve. Results: The proportion of international federation of gynecology and obstetrics (FIGO) stage IIA1~IIA2, levels of serum SCCA, TSGF, HE4, Kep and Ktrans in LNM group were higher than that in non LNM group (P<0.05). There was no significant difference in Ve between two groups (P>0.05). Kep, Ktrans, serum SCCA, TSGF, HE4 alone and in combination had high sensitivity and specificity in predicting LNM in patients with early cervical cancer, the area under the curve (AUC) of combined test in predicting LNM in patients with early cervical cancer was higher than that of each index alone. Conclusion: DCE-MRI quantitative parameters combined with serum SCCA, TSGF and HE4 have high predictive value for LNM in patients with early cervical cancer, and can be used for auxiliary predic- tion of LNM in early cervical cancer. [ABSTRACT FROM AUTHOR]

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with an extended Tofts linear (ETL) model for tissue and tumor evaluation has been established, but its effectiveness in evaluating the pancreas remains uncertain. Purpose: To understand the pharmacokinetics of normal pancreas and serve as a reference for future studies of pancreatic diseases. Material and Methods: Pancreatic pharmacokinetic parameters of 54 volunteers were calculated using DCE-MRI with the ETL model. First, intra- and inter-observer reliability was assessed through the use of the intra-class correlation coefficient (ICC) and coefficient of variation (CoV). Second, a subgroup analysis of the pancreatic DCE-MRI pharmacokinetic parameters was carried out by dividing the 54 individuals into three groups based on the pancreatic region, three groups based on age, and two groups based on sex. Results: There was excellent agreement and low variability of intra- and inter-observer to pancreatic DCE-MRI pharmacokinetic parameters. The intra- and inter-observer ICCs of K trans, k ep, ve, and vp were 0.971, 0.952, 0.959, 0.944 and 0.947, 0.911, 0.978, 0.917, respectively. The intra- and inter-observer CoVs of K trans, k ep, ve, vp were 9.98%, 5.99%, 6.47%, 4.76% and 10.15%, 5.22%, 6.28%, 5.40%, respectively. Only the pancreatic ve of the older group was higher than that of the young and middle-aged groups (P = 0.042, 0.001), and the vp of the pancreatic head was higher than that of the pancreatic body and tail (P = 0.014, 0.043). Conclusion: The application of DCE-MRI with an ETL model provides a reliable, robust, and reproducible means of non-invasively quantifying pancreatic pharmacokinetic parameters. [ABSTRACT FROM AUTHOR]

Background: To date, an effective means to preoperatively predict the malignant transformation of sinonasal inverted papilloma (SIP) remains lacking due to similarities in clinical appearance. This study aimed to retrospectively evaluate dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) parameters and microvessel structure in tumors with histologically confirmed SIP and inverted papilloma‐associated squamous cell carcinoma (IP‐SCC), as well as correlate DCE‐MRI findings with angiogenesis biomarkers. Methods: Absolute quantitative DCE‐MRI parameters (Ktrans, Kep, Ve) based on the Tofts model and model‐free semi‐quantitative indices (Tpeak, WR, MaxSlope) of SIP (n = 22) and IP‐SCC (n = 20) were investigated. Regions of interest (ROIs) were oriented according to the tumor subsites in the surgical records. Micro‐vessel density (MVD) counts and tight junction protein (claudin‐5) expression were evaluated in tumor specimens obtained during surgery. Differences in the above data were compared between the two groups. Correlations between DCE‐MRI parameters and angiogenic biomarkers were analyzed. Results: Compared with SIP specimens, IP‐SCC specimens were characterized by a significantly higher MVD and a leakier microvessel barrier. The values of Tpeak and Ve were significantly higher for SIP than those for IP‐SCC, whereas WR, MaxSlope, and Kep were significantly lower, indicating early enhancement and a faster dispersion model in IP‐SCC. MVD was positively correlated with WR and Kep and negatively correlated with Tpeak. Tpeak was slightly positively correlated to claudin‐5 expression. Conclusion: DCE‐MRI can serve as a noninvasive biomarker of angiogenesis in the malignant transformation from SIP to IP‐SCC. DCE‐MRI may assist in the differentiation of malignancies and treatment selection. [ABSTRACT FROM AUTHOR]

To study the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters combined with carcino-embryonic antigen (CEA), neutrophils and lymphocytes ratio (NLR), platelet-to-lymphocyte ratios (PLR) for the efficacy of neoadjuvant chemotherapy in cervical cancer patients. 110 cases of cervical cancer patients who received radical hysterectomy treatment in Zhangjiakou First Hospital from May 2020 to May 2023 were selected and divided into effective group and ineffective group based on the treatment effect. The general data, DCE-MRI parameters, serum CEA and peripheral blood NLR and PLR of the effective group and ineffective group were compared. The predictive value of DCE-MRI parameters combined with serum CEA, peripheral blood NLR and PLR on the efficacy of neoadjuvant chemotherapy was analyzed by Receiver Operating Characteristic (ROC) curve. Among the 110 patients selected, 78 were in the effective group, and 32 were in the ineffective group. The contrast enhancement rate (CER) and transport constant (Ktrans) levels in the effective group were lower than those in the ineffective group (P<0.05), but there were no significant differences in other DCE-MRI parameters rate constant (Kep), initial area under the gadolinium curve (IAUGC), percentage of extracellular space volume (Ve) and maximum slope (Maxslope) between the two groups (P>0.05). The serum CEA and peripheral blood NLR and PLR in the effective group were significantly lower than those in the ineffective group (P<0.05). CER, Ktrans combined with serum CEA and peripheral blood NLR and PLR had the best predictive value for the efficacy of neoadjuvant chemotherapy, with AUC was 0.896, and sensitivity was 0.915, specificity was 0.802, which were higher than each single detection. DCE-MRI parameters combined with CEA, NLR and PLR can help to improving predictive value of the efficacy of neoadjuvant chemotherapy in cervical cancer patients. [ABSTRACT FROM AUTHOR]

IntroductionTransplantation of kidneys from expanded criteria donors (ECD), including after circulatory death (DCD), is associated with a higher risk of adverse events compared to kidneys from standard criteria donors. In previous studies, improvements in renal transplant outcomes have been seen when kidneys were perfused with gaseous oxygen during preservation (persufflation, PSF). In the present study, we assessed ex-vivo renal function from a Diffusion Contrast Enhanced (DCE)-MRI estimation of glomerular filtration rate (eGFR); and metabolic sufficiency from whole-organ oxygen consumption (WOOCR) and lactate production rates.MethodsUsing a porcine model of DCD, we assigned one kidney to antegrade PSF, and the contralateral kidney to static cold storage (SCS), both maintained for 24 h at 4°C. Post-preservation organ quality assessments, including eGFR, WOOCR and lactate production, were measured under cold perfusion conditions, and biopsies were subsequently taken for histopathological analysis.ResultsA significantly higher eGFR (36.6 ± 12.1 vs. 11.8 ± 4.3 ml/min, p

Objective: To investigate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC) value combined with neutrophil-to-lymphocyte ratio (NLR) and free prostate specific antigen/total prostate specific antigen (fPSA/tPSA) in the diagnosis of prostate cancer (PCa). Methods: A total of 179 PCa patients admitted to Cheng- du Second People's Hospital from March 2021 to August 2023 were selected as PCa group and 163 patients with benign prostatic hyper- plasia (BPH) were selected as the BPH group. The DCE-MRI quantitative parameters, ADC values and peripheral blood NLR, and serum fPSA/tPSA were compared between the two groups. The receiver operating characteristic curve (ROC) was used to analyze the value of each index for diagnosing PCa. Results: Ktrans, Kep and NLR in PCa group were higher than those in BPH group (P<0.05), ADC and fPSA/tPSA were lower than those in BPH group (P<0.05). The area under the curve of PCa combined diagnosis was 0.956, which was higher than for diagnosis alone. Conclusion: The DCE-MRI parameters Ktrans, Kep and NLR of PCa patients increased, ADC and fPSA/tPSA decreased, The combination of DCE-MRI parameters, ADC, NLR and fPSA PSA can improve the diagnostic value of PCa. [ABSTRACT FROM AUTHOR]

Objective: To explore the effectiveness of machine learning classifiers based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes (TILs) in patients with advanced gastric cancer (AGC). Methods: This study investigated 103 patients with confirmed AGC through DCE-MRI and immunohistochemical staining. Immunohistochemical staining was used to evaluate CD3+, CD4+, and CD8+ T-cell expression. Utilizing Omni Kinetics software, radiomics features (Ktrans, Kep, and Ve) were extracted and underwent selection via variance threshold, SelectKBest, and LASSO methods. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) are the four classifiers used to build four machine learning (ML) models, and their performance was evaluated using 10-fold cross-validation. The model's performance was evaluated and compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results: In terms of CD3+, CD4+, and CD8+ T lymphocyte prediction models, the random forest model outperformed the other classifier models in terms of CD4+ and CD8+ T cell prediction, with AUCs of 0.913 and 0.970 on the training set and 0.904 and 0.908 on the validation set, respectively. In terms of CD3+ T cell prediction, the logistic regression model fared the best, with AUCs on the training and validation sets of 0.872 and 0.817, respectively. Conclusion: Machine learning classifiers based on DCE-MRI have the potential to accurately predict CD3+, CD4+, and CD8+ tumor-infiltrating lymphocyte expression levels in patients with AGC. [ABSTRACT FROM AUTHOR]

Introduction: The image quality of continuously acquired free-breathing Dynamic Contrast-Enhanced (DCE) golden-angle radial Magnetic Resonance Imaging (MRI) of abdomen suffers from motion artifacts and motion-related blurring. We propose a scheme by minimizing patients' motion status from breathing as well as optimizing the acquiring parameters to improve image quality and diagnostic performance of DCE-MRI with Golden-Angle Radial Sparse Parallel (GRASP) sequence of abdomen. Methods: The optimization scheme follows two principles: (1) reduce the impact on images from unpredictable and irregulate motions during examination and (2) adjust the sequence parameters to increase the number of radial views in each partition. For the assessment of image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the severity of radial artifact, the degree of image sharpness, and a visual scoring of image quality with a 5-point scale were assessed. Results: A total of 64 patients were included in this study before (16 men, 14 women, age: 54.9 ± 17.0) and after (18 men, 16 women, age: 58.6 ± 12.6) the optimization scheme was performed. The results showed that the SNR values of right and left lobe of liver in both plain phase and arterial phase were significantly increased (All P < 0.001) after the GRASP sequence been optimized. Significant improvements in CNR values were observed in the arterial phase (All P < 0.05). The significant differences in scores at each phase for visual scoring of image quality, noise of the right and left lobe of liver, radial artifact, and sharpness indicating that the image quality was significantly improved after the optimization (All P < 0.001). Conclusion: Our study demonstrated that the optimized scheme significantly improved the image quality of liver DCE-MRI with GRASP sequence both in plain and arterial phases. The optimized scheme of GRASP sequence could be a superior alternative to conventional approach for the assessment of liver. [ABSTRACT FROM AUTHOR]

Background: Since no studies compared the value of radiomics features of distinct phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting triple-negative breast cancer (TNBC). Purpose: To identify the optimal phase of DCE-MRI for diagnosing TNBC and, in combination with clinical factors, to develop a clinical-radiomics model to well predict TNBC. Material and Methods: This retrospective study included 158 patients with pathology-confirmed breast cancer, including 38 cases of TNBC. The patients were randomly divided into the training and validation set (7:3). Eight radiomics models were built based on eight DCE-MR phases, and their performances were evaluated using receiver operating characteristic curve (ROC) and DeLong's test. The Radscore derived from the best radiomics model was integrated with independent clinical risk factors to construct a clinical-radiomics predictive model, and evaluate its performance using ROC analysis, calibration, and decision curve analyses. Results: WHO classification, margin, and T2-weighted (T2W) imaging signals were significantly correlated with TNBC and independent risk factors for TNBC (P <0.05). The clinical model yielded areas under the curve (AUCs) of 0.867 and 0.843 in the training and validation sets, respectively. The radiomics model based on DCEphase7 achieved the highest efficacy, with an AUC of 0.818 and 0.777. The AUC of the clinical-radiomics model was 0.936 and 0.886 in the training and validation sets, respectively. The decision curve showed the clinical utility of the clinical-radiomics model. Conclusion: The radiomics features of DCE-MRI had the potential to predict TNBC and could improve the performance of clinical risk factors for preoperative personalized prediction of TNBC. [ABSTRACT FROM AUTHOR]

Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration–time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade. [ABSTRACT FROM AUTHOR]

A 70-year-old man with supraglottic carcinoma underwent computed tomography (CT) for staging purposes. A tumor measuring approximately 7 × 10 cm was found incidentally in the left perirenal space. The tumor showed homogeneous high signal intensity on chemical shift subtraction magnetic resonance imaging (CSS-MRI) suggesting the presence of minimal amounts of fat. Five months later, the tumor had grown to approximately 10 × 12 cm with indistinct margins. CSS-MRI showed high signal intensity in the tumor periphery only. The tumor was resected and the pathological diagnosis was angiosarcoma. Angiosarcomas are malignant endothelial vascular neoplasms that are highly invasive to their surroundings. Here we report a case of primary perirenal angiosarcoma that was difficult to differentiate from a dedifferentiated liposarcoma. On CSS-MRI, high signal intensity within a tumor may be a characteristic feature of primary perirenal angiosarcoma.

Abstract Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (v e ), volume fraction of plasma space (v p ), volume transfer constant (K trans ), and reverse reflux rate constant (k ep ) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

This research aimed to assess the relationship between contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) values and dynamic contrast-enhanced (DCE) MRI parameters including (Ktrans, Kep, Ve, and iAUC). To evaluate the correlation between the MRF-derived values (T1 and T2 values, CE T1 and T2 values, T1 and T2 change) and DCE-MRI parameters and the differences in the parameters between prostate cancer and noncancer lesions in 68 patients, two radiologists independently drew regions-of-interest (ROIs) at the focal prostate lesions. Prostate cancer was identified in 75% (51/68) of patients. The CE T2 value was significantly lower in prostate cancer than in noncancer lesions in the peripheral zone and transition zone. Ktrans, Kep, and iAUC were significantly higher in prostate cancer than noncancer lesions in the peripheral zone (p < 0.05), but not in the transition zone. The CE T1 value was significantly correlated with Ktrans, Ve, and iAUC in prostate cancer, and the CE T2 value was correlated to Ve in noncancer. Some CE MRF values are different between prostate cancer and noncancer tissues and correlate with DCE-MRI parameters. Prostate cancer and noncancer tissues may have different characteristics regarding contrast enhancement.

ObjectiveTo explore the effectiveness of machine learning classifiers based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the expression levels of CD3+, CD4+, and CD8+ tumor-infiltrating lymphocytes (TILs) in patients with advanced gastric cancer (AGC).MethodsThis study investigated 103 patients with confirmed AGC through DCE-MRI and immunohistochemical staining. Immunohistochemical staining was used to evaluate CD3+, CD4+, and CD8+ T-cell expression. Utilizing Omni Kinetics software, radiomics features (Ktrans, Kep, and Ve) were extracted and underwent selection via variance threshold, SelectKBest, and LASSO methods. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) are the four classifiers used to build four machine learning (ML) models, and their performance was evaluated using 10-fold cross-validation. The model’s performance was evaluated and compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.ResultsIn terms of CD3+, CD4+, and CD8+ T lymphocyte prediction models, the random forest model outperformed the other classifier models in terms of CD4+ and CD8+ T cell prediction, with AUCs of 0.913 and 0.970 on the training set and 0.904 and 0.908 on the validation set, respectively. In terms of CD3+ T cell prediction, the logistic regression model fared the best, with AUCs on the training and validation sets of 0.872 and 0.817, respectively.ConclusionMachine learning classifiers based on DCE-MRI have the potential to accurately predict CD3+, CD4+, and CD8+ tumor-infiltrating lymphocyte expression levels in patients with AGC.

BACKGROUND: At present, digital subtraction angiography is used in the clinical observation of blood supply of femoral head necrosis, and the patency of the nutrient blood vessels of the femoral head is evaluated by observing the in and out of the contrast agent. Although digital subtraction angiography can visually observe the feeding vessels of the femoral head, it cannot quantify the perfusion. In addition, the collapse of the femoral head is closely related to the prognosis of the disease, but there are few studies on the changes in blood supply in the femoral head during the peri-collapse period. OBJECTIVE: To compare the differences in microcirculation perfusion in different regions and different stages of femoral head necrosis, summarize the changes in microcirculation perfusion characteristics of femoral head necrosis, and observe the effect of dynamic contrast-enhanced magnetic resonance imaging in evaluating the microcirculation perfusion of the femoral head. METHODS: From February 16 to September 1, 2022, a total of 37 patients (69 hips) who visited a doctor for femoral head necrosis were retrospectively collected, including 29 males and 8 females; the age was (35.35±12.19) years (range 18-65 years). The patient’s bilateral femoral head and proximal femur were scanned by dynamic contrast-enhanced magnetic resonance imaging, and gadoteric acid meglumine was intravenously injected during the scanning. After scanning, Geniq software was used to post-process the data to obtain femoral head microcirculation perfusion parameters. Regions of interest were drawn at the necrotic zone, repair reaction zone, and greater trochanter, and time-signal intensity curves of the regions of interest were obtained and classified. Paired t-tests were used to evaluate differences between necrotic area, repair reaction area, and greater trochanter perfusion parameters, as well as differences in femoral head perfusion in different stages of necrosis. Differences in necrotic area, repair reaction area and greater trochanter time-intensity curve classification were evaluated using the χ2 test. RESULTS AND CONCLUSION: (1) Comparing the perfusion parameters in different regions, the peak value of enhanced signal intensity, area under the curve, maximum slope, volume transfer constant, and the median volume fraction of extravascular extracellular space in the necrotic region were lower than those of the greater trochanter, and the differences were statistically significant (P < 0.05). There was no statistically significant difference in the volume rate constant (P > 0.05). (2) The signal peak value, area under the curve, maximum slope, volume transfer constant, rate transfer constant, and the median ratio of extravascular to extracellular space in the repair reaction area were higher than those in the necrotic area and the greater trochanter, and the differences were statistically significant (P < 0.05). (3) The median signal peak value and maximum slope of the necrotic area in ARCO stage II were greater than those in ARCO stage III, and the difference was statistically significant (P < 0.05). There were no significant differences in area under the curve, rate transfer constant, volume transfer constant and the median ratio of extravascular to extracellular space (P > 0.05). (4) The signal peak value, area under the curve, maximum slope, volume transfer constant, rate transfer constant, and ratio of extravascular to extracellular space in the repair reaction zone of the ARCO Stage II were lower than those of the ARCO Stage III, and the difference was statistically significant (P < 0.05). (5) It is concluded that the microcirculation perfusion velocity, blood flow and capillary permeability in the necrotic area of the femoral head were significantly reduced, while the microcirculation perfusion velocity, blood flow and vascular permeability in the repair reaction area were significantly improved compared with those in the normal area. With the occurrence of collapse, the perfusion velocity and blood flow were further reduced, and the perfusion in the repair reaction zone was enhanced. [ABSTRACT FROM AUTHOR]

Objective. Tracer kinetic models allow for estimating pharmacokinetic (PK) parameters, which are related to pathological characteristics, from breast dynamic contrast-enhanced magnetic resonance imaging. However, existing tracer kinetic models subject to inaccuracy are time-consuming for PK parameters estimation. This study aimed to accurately and efficiently estimate PK parameters for predicting molecular subtypes based on convolutional neural network (CNN). Approach. A CNN integrating global and local features (GL-CNN) was trained using synthetic data where known PK parameters map was used as the ground truth, and subsequently used to directly estimate PK parameters (volume transfer constant K trans and flux rate constant K ep) map. The accuracy assessed by the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and concordance correlation coefficient (CCC) was compared between the GL-CNN and Tofts-based PK parameters in synthetic data. Radiomic features were calculated from the PK parameters map in 208 breast tumors. A random forest classifier was constructed to predict molecular subtypes using a discovery cohort (n = 144). The diagnostic performance evaluated on a validation cohort (n = 64) using the area under the receiver operating characteristic curve (AUC) was compared between the GL-CNN and Tofts-based PK parameters. Main results. The average PSNR (48.8884), SSIM (0.9995), and CCC (0.9995) between the GL-CNN-based K trans map and ground truth were significantly higher than those between the Tofts-based K trans map and ground truth. The GL-CNN-based K trans obtained significantly better diagnostic performance (AUCs = 0.7658 and 0.8528) than the Tofts-based K trans for luminal B and HER2 tumors. The GL-CNN method accelerated the computation by speed approximately 79 times compared to the Tofts method for the whole breast of all patients. Significance. Our results indicate that the GL-CNN method can be used to accurately and efficiently estimate PK parameters for predicting molecular subtypes. [ABSTRACT FROM AUTHOR]

This research aimed to assess the relationship between contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) values and dynamic contrast-enhanced (DCE) MRI parameters including (Ktrans, Kep, Ve, and iAUC). To evaluate the correlation between the MRF-derived values (T1 and T2 values, CE T1 and T2 values, T1 and T2 change) and DCE-MRI parameters and the differences in the parameters between prostate cancer and noncancer lesions in 68 patients, two radiologists independently drew regions-of-interest (ROIs) at the focal prostate lesions. Prostate cancer was identified in 75% (51/68) of patients. The CE T2 value was significantly lower in prostate cancer than in noncancer lesions in the peripheral zone and transition zone. Ktrans, Kep, and iAUC were significantly higher in prostate cancer than noncancer lesions in the peripheral zone (p < 0.05), but not in the transition zone. The CE T1 value was significantly correlated with Ktrans, Ve, and iAUC in prostate cancer, and the CE T2 value was correlated to Ve in noncancer. Some CE MRF values are different between prostate cancer and noncancer tissues and correlate with DCE-MRI parameters. Prostate cancer and noncancer tissues may have different characteristics regarding contrast enhancement. [ABSTRACT FROM AUTHOR]

Objective: To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with serum tumor supplied group of factors (TSGF), type Ⅰ collagen carboxyl pyridine terminal peptide (ICTP) and alkaline phosphatase (ALP) in the identification of benign and malignant bone tumors and the efficacy evaluation of bone malignant tumors after treatment.Methods: 163 patients with bone tumors who were admitted to our hospital from December 2020 to April 2022 were selected as research subjects, and patients were divided into benign group (74 cases) and malignant group (89 cases) according to the benign and malignant bone tumors. Malignant group was divided into good efficacy group and poor efficacy group according to the evaluation criteria of solid tumor efficacy after treatment. The volume transfer constant (Ktrans), rate constant (Kep), extravascular extracellular space volume fraction (Ve), area under initial curve within 1 minute (i AUC), TSGF, ICTP and ALP were compared between benign group and malignant group, good efficacy group and poor efficacy group. The correlation between Ktrans, Kep, Ve, i AUC and serum TSGF, ICTP,ALP was analyzed. The predictive value of each index for benign and malignant bone tumors were analyzed by receiver operating characteristic (ROC) curve. Results: Ktrans, Kep, i AUC, TSGF, ICTP and ALP were compared between different groups, benign group was lower than that of malignant group (P＜0.05), good efficacy group was lower than that of poor efficacy group (P＜0.05) ; Ktrans, Kep and i AUC in DCE-MRI quantitative parameters were positively correlated with serum TSGF, ICTP and ALP (P＜0.05) . The area under the curve (AUC) of Ktrans, Kep, i AUC, TSGF, ICTP, ALP and combined prediction of benign and malignant bone tumors were 0.794,0.804, 0.744, 0.747, 0.773, 0.828 and 0.986 respectively, and the AUC of the combined prediction of benign and malignant bone tumors was greater than that of each index alone. Conclusion: DCE-MRI quantitative parameters Ktrans, Kep, i AUC combine with serum TSGF,ICTP and ALP have high value in differentiating benign and malignant bone tumors, the higher the Ktrans, Kep, i AUC, TSGF, ICTP, and ALP, the worse the clinical efficacy of patients with malignant bone tumors after treatment. [ABSTRACT FROM AUTHOR]

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis. [ABSTRACT FROM AUTHOR]

Abstract Introduction Similar white matter hyperintensities (WMH) burden may have varied cognitive outcomes in patients with cerebral small vessel disease (CSVD). This study aimed to evaluate whether blood–brain barrier (BBB) permeability is associated with cognitive impairment (CI) heterogeneity in patients with WMH. Methods We recruited 51 participants with WMH. We evaluated WMH burden using the Fazekas scale and WMH volume on structural magnetic resonance imaging (MRI), and assessed BBB permeability using dynamic contrast-enhanced (DCE)-MRI. We used permeability–surface area product (PS) from the Patlak model to represent BBB permeability. All patients underwent Mini-Mental State Examination (MMSE), Boston Naming Test (BNT) and animal verbal fluency test (VFT) for cognitive assessment. We divided patients into CI and non-CI groups based on their MMSE scores (

Abstract Background To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. Methods This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (K trans , K ep and V e ) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive). Results The mean values of K trans and V e in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in K trans skewness, K trans entropy, K trans kurtosis, and V e maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The K trans kurtosis and K trans entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores. Conclusions Histogram Analysis of DCE-MRI K trans maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores.

Introduction: Similar white matter hyperintensities (WMH) burden may have varied cognitive outcomes in patients with cerebral small vessel disease (CSVD). This study aimed to evaluate whether blood–brain barrier (BBB) permeability is associated with cognitive impairment (CI) heterogeneity in patients with WMH. Methods: We recruited 51 participants with WMH. We evaluated WMH burden using the Fazekas scale and WMH volume on structural magnetic resonance imaging (MRI), and assessed BBB permeability using dynamic contrast-enhanced (DCE)-MRI. We used permeability–surface area product (PS) from the Patlak model to represent BBB permeability. All patients underwent Mini-Mental State Examination (MMSE), Boston Naming Test (BNT) and animal verbal fluency test (VFT) for cognitive assessment. We divided patients into CI and non-CI groups based on their MMSE scores (< 27 or ≥ 27) and used multiple linear regression models to investigate the associations between MRI parameters and cognitive function. Results: Patients in the two groups did not differ in Fazekas scores and WMH volume. However, patients in the CI group showed significantly higher PS in the WMH regions than those in non-CI group (1.89 × 10−3 versus 1.00 × 10−3, p = 0.032 in periventricular WMH [PVWMH]; 1.27 × 10−3 versus 0.74 × 10−3, p = 0.043 in deep WMH [DWMH]), indicating the breakdown of BBB in the CI group. In all patients with WMH, increased BBB permeability in PVWMH and DWMH was significantly associated with lower cognitive and language function after adjustment for age, education level (EL) and intracranial volume (ICV). In the CI group, this correlation remained significant. WMH volume was not associated with cognitive performance in either all patients or those with CI. Conclusion: BBB impairment might be a more sensitive indicator for cognitive and language dysfunction than WMH volume in patients with WMH and possibly explains the heterogeneity of cognitive performance in patients with similar WMH burden. [ABSTRACT FROM AUTHOR]

Purpose: The optimal temporal resolution for free-breathing dynamic contrast-enhanced MRI (FBDCEMRI) of the pancreas has not been determined. This study aimed to evaluate the appropriate temporal resolution to achieve good image quality and to perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using golden-angle radial sparse parallel (GRASP). Methods: Sixteen participants (53 ± 15 years, eight females) undergoing FBDCE-MRI were included in this prospective study. Images were retrospectively reconstructed at four temporal resolutions (1.8, 3.0, 4.8, and 7.8s). Two radiologists (5 years of experience) evaluated the image quality of each reconstructed image by assessing the visualization of the celiac artery (CEA), the common hepatic artery, the splenic artery, each area of the pancreas, and artifacts using a 5-point scale. Using Tissue-4D, pharmacokinetic parameters were calculated for each area in the reconstructed images at each temporal resolution for 16 examinations, excluding two with errors in the pharmacokinetic modeling analysis. Friedman and Bonferroni tests were used for analysis. A P value < 0.05 was considered statistically significant. Results: During vascular assessment, only scores for the CEA at 7.8s were significantly lower than the other temporal resolutions. Scores of all pancreatic regions and artifacts were significantly lower at 1.8s than at 4.8s and 7.8s. In the pharmacokinetic analysis, all volume transfer coefficients (Ktrans), rate constants (Kep), and the initial area under the concentration curve (iAUC) in the pancreatic head and tail were significantly lower at 4.8s and 7.8s than at 1.8s. iAUC in the pancreatic body and extracellular extravascular volume fraction (Ve) in the pancreatic head were significantly lower at 7.8s than at 1.8s. Conclusion: A temporal resolution of 3.0s is appropriate to achieve image quality and perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using GRASP. [ABSTRACT FROM AUTHOR]

Background: Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. Purpose: To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. Material and Methods: This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t -test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. Results: Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp−90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05). Conclusion: Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status. [ABSTRACT FROM AUTHOR]

Breast tumor segmentation based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging is significant step for computable radiomics analysis of breast cancer. Manual tumor annotation is time-consuming process and involves medical acquaintance, biased, inclined to error, and inter-user discrepancy. A number of modern trainings have revealed the capability of deep learning representations in image segmentation. Here, we describe a 3D Connected-UNets for tumor segmentation from 3D Magnetic Resonance Imagings based on encoder-decoder architecture. Due to a restricted training dataset size, a variational auto-encoder outlet is supplementary to renovate the input image itself in order to identify the shared decoder and execute additional controls on its layers. Based on initial segmentation of Connected-UNets, fully connected 3D provisional unsystematic domain is used to enhance segmentation outcomes by discovering 2D neighbor areas and 3D volume statistics. Moreover, 3D connected modules evaluation is used to endure around large modules and decrease segmentation noise. The proposed method has been assessed on two widely offered datasets, explicitly INbreast and the curated breast imaging subset of digital database for screening mammography The proposed model has also been estimated using a private dataset. The experimental results show that the proposed model outperforms the state-of-the-art methods for breast tumor segmentation. [ABSTRACT FROM AUTHOR]

Repetitive mild traumatic brain injury in American football players has garnered increasing public attention following reports of chronic traumatic encephalopathy, a progressive tauopathy. While the mechanisms underlying repetitive mild traumatic brain injury-induced neurodegeneration are unknown and antemortem diagnostic tests are not available, neuropathology studies suggest a pathogenic role for microvascular injury, specifically blood-brain barrier dysfunction. Thus, our main objective was to demonstrate the effectiveness of a modified dynamic contrast-enhanced MRI approach we have developed to detect impairments in brain microvascular function. To this end, we scanned 42 adult male amateur American football players and a control group comprising 27 athletes practicing a non-contact sport and 26 non-athletes. MRI scans were also performed in 51 patients with brain pathologies involving the blood-brain barrier, namely malignant brain tumours, ischaemic stroke and haemorrhagic traumatic contusion. Based on data from prolonged scans, we generated maps that visualized the permeability value for each brain voxel. Our permeability maps revealed an increase in slow blood-to-brain transport in a subset of amateur American football players, but not in sex- and age-matched controls. The increase in permeability was region specific (white matter, midbrain peduncles, red nucleus, temporal cortex) and correlated with changes in white matter, which were confirmed by diffusion tensor imaging. Additionally, increased permeability persisted for months, as seen in players who were scanned both on- and off-season. Examination of patients with brain pathologies revealed that slow tracer accumulation characterizes areas surrounding the core of injury, which frequently shows fast blood-to-brain transport. Next, we verified our method in two rodent models: rats and mice subjected to repeated mild closed-head impact injury, and rats with vascular injury inflicted by photothrombosis. In both models, slow blood-to-brain transport was observed, which correlated with neuropathological changes. Lastly, computational simulations and direct imaging of the transport of Evans blue-albumin complex in brains of rats subjected to recurrent seizures or focal cerebrovascular injury suggest that increased cellular transport underlies the observed slow blood-to-brain transport. Taken together, our findings suggest dynamic contrast-enhanced-MRI can be used to diagnose specific microvascular pathology after traumatic brain injury and other brain pathologies.

Background Heavy menstrual bleeding affects one in four women and negatively impacts quality of life. The levonorgestrel-releasing intrauterine system is an effective long-term treatment but is discontinued by many due to unpredictable bleeding, or adverse effects. The selective progesterone receptor modulator ulipristal acetate is used to treat symptomatic fibroids but long-term efficacy for the symptom of heavy menstrual bleeding, irrespective of presence of fibroids, is unknown. Objectives To determine whether ulipristal acetate is more effective at reducing the burden of heavy menstrual bleeding than levonorgestrel-releasing intrauterine system after 12 months of treatment in women with and without fibroids. We investigated mechanism of action of ulipristal acetate in a subset of 20 women. Design Randomised, open-label, parallel group, multicentre trial with embedded mechanistic study. Setting Ten UK hospitals. Participants Women with heavy menstrual bleeding aged 18 and over with no contraindications to levonorgestrel-releasing intrauterine system or ulipristal acetate. Interventions Three 12-week treatment cycles of 5 mg ulipristal acetate daily, separated by 4-week treatment-free intervals, or continuous levonorgestrel-releasing intrauterine system following allocation in a 1 : 1 ratio using a web-based minimisation procedure. Main trial outcome measures Primary outcome was quality-of-life measured by menorrhagia multi-attribute scale at 12 months. Secondary outcomes included menstrual bleeding and patient satisfaction. Impact on fibroid size, endometrial appearance and liver function was also collected. Mechanistic study outcome Cellular markers for endometrial cell structure and function, determined from endometrial biopsies; volume of uterus and fibroids and microcirculation parameters were determined from magnetic resonance images. Results Sample size was increased from 220 to 302 as a result of temporary halt to recruitment due to concerns of ulipristal acetate hepatoxicity. Subsequent withdrawal of ulipristal acetate and the COVID-19 pandemic led to a premature closure of recruitment, with 118 women randomised to each treatment and 103 women completing 12-month menorrhagia multi-attribute scale scores prior to this point. Primary outcome scores substantially improved in both arms, but at 12 months there was no evidence of a difference between those receiving three cycles of ulipristal acetate [median score category: 76–99, interquartile range (51–75 to 100), n = 53] and levonorgestrel-releasing intrauterine system [median score category: 76–99, interquartile range (51–75 to 100), n = 50; adjusted odds ratio 0.55, 95% confidence interval 0.26 to 1.17; p = 0.12]. Rates of amenorrhoea were much higher in those allocated ulipristal acetate compared with the levonorgestrel-releasing intrauterine system (12 months: 64% vs. 25%, adjusted odds ratio 7.12, 95% confidence interval 2.29 to 22.2). There was no evidence of a difference in other participant-reported outcomes. There were no cases of endometrial malignancy and no hepatotoxicity due to ulipristal acetate use. Mechanistic study results Ulipristal acetate produced a reversible reduction in endometrial cell proliferation, as well as reversible alteration of other endometrial cellular markers. Ulipristal acetate did not produce a reduction in the volume of the uterus irrespective of coexisting fibroids, nor an effect on uterine microvascular blood flow. Limitations The urgent safety measures and premature closure of recruitment impacted final sample size. Conclusions We found no evidence of a difference in quality of life between the two treatments, but ulipristal acetate was superior to levonorgestrel-releasing intrauterine system at inducing amenorrhoea. Ulipristal acetate currently has restricted availability due to concerns regarding hepatotoxicity. Future work There is a need to develop new, safe, effective and fertility-sparing medical treatments for heavy menstrual bleeding. The observed acceptability and effectiveness of ulipristal acetate warrants further research into the selective progesterone receptor modulator class of pharmacological agents. Study registration This trial is registered as ISRCTN 20426843. Plain language summary What is the problem? Heavy menstrual bleeding is a common condition that affects the lives of many women. A hormone-releasing coil, fitted inside the womb, is effective in making periods lighter but can make them less regular. A medicine called ulipristal acetate or UPA, taken as a pill, has been shown to reduce rapidly menstrual bleeding in women with large, non-cancerous tumours in the womb, known as fibroids. It was not known whether UPA is effective in women who have heavy periods but do not have fibroids of any significant size. What did we plan to do? To find out which treatment was better at controlling heavy periods, 236 women were enrolled in a clinical trial where they received either the hormone coil or UPA. The choice of treatment was made at random by a computer rather than the wishes of researchers or patients, to ensure a fair comparison. Participants completed questionnaires about their symptoms and life quality at intervals up to 1 year. Twice during the trial, medicines regulatory authorities raised safety concerns about UPA causing liver problems. This resulted in the introduction of regular blood tests. The second time, recruitment to the trial stopped early. What did we find? Both treatments improved the symptoms of heavy menstrual bleeding in the majority of women. We found no evidence that UPA was better overall after 1 year of treatment, compared with the hormone coil, although fewer women on UPA continued to have periods. Laboratory studies on samples taken from the lining of the womb showed temporary changes due to UPA, which disappeared after treatment stopped. What does this mean? Both treatments improve the symptoms of menstrual bleeding and general wellbeing. Because of safety concerns UPA is not available for all women with heavy menstrual bleeding and new, safer medical treatments are needed. Scientific summary Background Heavy menstrual bleeding (HMB) is the most common gynaecological problem in women of reproductive age, affecting one in four women, and has adverse profound impact on health-related quality of life. Common causes of HMB include structural abnormalities such as uterine fibroids, adenomyosis and dysfunction of the endometrium. The levonorgestrel-releasing intrauterine system (LNG-IUS) is a proven, effective long-term treatment but about one-third of women cease use by two years due to unpredictable bleeding, hormonal adverse effects or lack of effectiveness. Furthermore, fibroids can make the LNG-IUS less effective. Alternative medical options for HMB exist, but are either less effective or associated with unacceptable adverse effects. Surgical interventions are effective at inducing bleeding control and improving quality of life but are typically incompatible with future fertility. Effective long-term medical treatments for women with HMB are needed. A class of drugs called selective progesterone receptor modulators (SPRMs) have potential to provide an effective oral treatment for HMB. SPRMs bind with progesterone receptors, resulting in tissue-specific effects in both myometrial and overlying endometrial tissue as well as shrinking uterine fibroids. The SPRM ulipristal acetate (UPA) has been successfully used to treat fibroids, but we do not know how effective UPA is for the treatment of women with HMB who do not have fibroids. Furthermore, there are uncertainties regarding the mechanism and location of action of UPA, as well as its longer-term safety. SPRMs induce distinctive, non-physiological endometrial changes, which can be confused with endometrial hyperplasia. More recently there has been concern regarding the potential for UPA to cause drug-induced liver injury (DILI). Post marketing surveillance reports resulted in a temporary halt in UPA use in 2018 and 2020. Use of UPA has since been reinstated since January 2021, albeit in a restricted context, reflecting the paucity of existing alternatives for HMB. Given these uncertainties, we designed the UCON trial to evaluate the safety, tolerability and effectiveness of UPA on HMB and to understand its mechanism of action. Clinical objectives Primary objective: to determine whether UPA is more effective at reducing the burden of HMB symptoms than LNG-IUS after 12 months of treatment. Secondary objectives: Ascertain whether UPA use beyond 3 months’ and up to 12 months’ duration is associated with histological changes to the endometrium and, if so, whether this compromises safety. Ascertain whether UPA is more effective than LNG-IUS in relation to menstrual blood loss, sexual activity, generic quality of life, satisfaction with treatment, patient-reported adverse events, and compliance at 3, 6 and 12 months. Determine the response to UPA and LNG-IUS treatment difference in the presence of uterine fibroids in terms of (1) alleviation of HMB and (2) change in uterine/fibroid volume. Collect data on liver function in women taking UPA, once safety concerns were raised. Mechanism of action study objectives To understand how UPA causes a reduction in menstrual bleeding and uterine/fibroid volume in women with HMB, we determined whether UPA administration: Alters endometrial cell function (e.g., and not limited to, proliferation, apoptosis, expression of steroid receptors, tumour suppressors and inflammatory mediators). Reduces blood plasma flow in the endometrium, uterine myometrium and fibroid tissue. Alters the volume fraction of the extracellular matrix in these tissues. Reduces uterine and fibroid volume. Design This was a randomised, open-label, parallel group, multicentre trial with embedded mechanistic study. Methods Setting The trial recruited participants in 10 sites in NHS hospital settings across the UK between 2015 and 2020. The mechanism of action study was conducted solely at the Edinburgh site. Participants For the main trial, informed consent was sought from premenopausal women (aged 18–50 years) with self-reported HMB, no contraindications to LNG-IUS or UPA. Those with uterine size greater than equivalent 14-week size or with submucosal fibroids >2cm were excluded. Other exclusion criteria relating to use of other treatments and current health status were applied, including history of severe hepatic impairment. Screening and randomisation Participants were recruited in gynaecology clinics by research nurses who screened patient referral letters. Following consent, haemoglobin and circulating estradiol levels were assessed, clinical history elicited and transvaginal and/or abdominal ultrasound and endometrial biopsy were obtained if not previously performed. Following this, and confirmation of eligibility, randomisation was via a web-based central service based at Birmingham Clinical Trials Unit to allocate women in a 1 : 1 ratio using a minimisation algorithm. Screened patients in Edinburgh were offered the opportunity to participate in the mechanistic study. Interventions and follow-up Those allocated to UPA received three courses of treatment, each course comprising a daily 5-mg oral dose for 12 weeks followed by a four-week break. Those allocated to the LNG-IUS had it fitted in hospital or primary care. Participants allocated to UPA returned to hospital to collect their repeat prescription at 3 and 6 months, and may have been seen by a member of the care team if required. They were then seen in clinic at 12 months for ultrasound scan (USS) and haemoglobin/serum estradiol measurement. Those allocated to the LNG-IUS group attended USS at 12 months. Follow-up at interim time points was conducted by postal questionnaire. Those partaking in the mechanism of action study underwent magnetic resonance imaging (MRI) following randomisation and at the end of treatment cycles two and three. An additional endometrial biopsy was obtained at the end of treatment cycle two. Outcome measures Primary Condition-specific quality of life score as measured by the menorrhagia multi-attribute scale (MMAS) questionnaire at 12 months. Summary scores range from 0 (worst affected) to 100 (not affected). Secondary Condition-specific quality of life score as measured by MMAS at 3 and 6 months Menstrual bleeding (pictorial blood loss assessment chart)* Cycle regularity (ordinal four-point scale)* Duration of period (ordinal three option scale)* Pelvic pain during periods, intercourse and at other times (visual analogue scales; 0 = best outcome, 10 = worse outcome)* Uterine fibroid symptom and quality of life instrument (only given to women diagnosed with fibroids)* Sexual function (sexual activity questionnaire)* Generic quality of life (EQ-5D-5L)* Satisfaction with treatment outcome (five-point Likert scale) Participant rating of effect of treatment on HMB over 12 months (four-point Likert scale) Whether participant is willing to recommend the treatment to a friend (yes/no) Surgical intervention Adherence to trial treatments and reasons for changing treatment, as reported by the participant Serious adverse events and reactions Uterine volume, evidence of adenomyosis, presence of fibroids, largest fibroid volume, endometrial thickness, endometrial appearance, evidence of ovarian cysts at 12 months (USS) Endometrial biopsy at 12 months (UPA group only) Liver function tests, from 20 March 2018 every four weeks (UPA group only) Haemoglobin and serum estradiol at 12 months * assessed at 3, 6 and 12 months Mechanism of action A: Effects on cellular markers of endometrial steroid receptors and metabolising enzymes (governing local endometrial steroid [ligand] availability), cell proliferation, cell survival (apoptosis); detection of genes implicated in control of proliferation in endometrium; B: Effects on uterine/fibroid structure addressed by obtaining volume measurements for the whole uterus, and for the total volume of fibroids when present, by using high resolution structural MRI and stereology; and C: Uterine vascularity using dynamic contrast-enhanced MRI (DCE-MRI). Urgent safety measures In November 2017, the European Medicines Agency (EMA) issued an urgent drug alert for UPA due to a small number of reports of serious liver injury. A detailed investigation by the regulatory authorities was undertaken and it was found that eight reports of serious liver injury were reported in Europe from an estimated 740,000 women using UPA for uterine fibroids. Restrictions on prescribing UPA were subsequently issued and the trial sponsor implemented an urgent safety measure (USM) in February 2018, which halted recruitment. Those allocated UPA were allowed to complete their current course of UPA treatment but not commence any further outstanding courses. In addition, they commenced monthly assessment of LFTs (as well as a post treatment test approximately 2 weeks after the last course of UPA). In August 2018, the halt on UPA prescribing was lifted and recruitment to UCON resumed in October 2018 with additional safety measures in place, including exclusion of those with any history of liver disease [defined as levels of alanine transaminase (ALT) or aspartate aminotransferase (AST) of more than two times the upper limit of normal] and LFT monitoring as described above. UPA was stopped if women had an ALT or AST more than three times the upper limit of normal and a hepatology opinion was sought. In March 2020, the EMA temporarily suspended use of UPA a second time due to ongoing concerns regarding hepatotoxicity and a further USM was issued. All treatment courses of UPA were immediately stopped. In view of the second USM, the investigators, in discussion with the funder, chose premature closure of recruitment to the study but planned follow-up actions continued as per protocol. Statistical considerations The study was powered to detect a clinically useful difference in MMAS score (13 points) between the two groups at twelve months. To detect a difference of this size [0.5 standard deviations (SDs)] with 90% power (p = 0.05) would require 86 women in each group (172 in total). To allow for a 20% loss to follow-up or pregnancy, the sample size was inflated to 220 women. Following the initial USM, this figure was inflated to 302 women to ensure that there were adequate responses in the primary analysis population (defined below) to detect the same size of difference. The original planned primary analysis population comprised all participants, regardless of adherence to treatment, employing suitable regression models to estimate difference between groups. The enforced non-compliance as a result of the withdrawal of UPA had substantial implications for the validity of the data reported by participants. It was therefore necessary to redefine analysis populations, considering the restrictions that prevented women taking their courses of UPA might influence their responses and any other new potential biases that may be apparent in either group due to, for example, knowledge of the safety concerns around UPA. The primary analysis population would now comprise participants with questionnaire responses received prior to the first USM (12 February 2018), along with questionnaire responses from participants recruited following the study restart (18 October 2018) provided that the responses were returned before the second USM (17 March 2020). Results Main trial A total of 4471 women were approached for the study, with 236 consented and randomised, of whom 181 (77%) returned primary outcome data at 12 months (103 within the primary analysis population). Baseline data were comparable between groups; 92% were white, 34% had fibroids and 8% adenomyosis. In the primary analysis population, MMAS scores substantially improved in both arms, but at 12 months there was no evidence of a difference between the UPA [median score category: 76–99, IQR (51–75 to 100), n = 53] and LNG-IUS [median score category: 76–99, IQR (51–75 to 100), n = 50] groups (adjusted OR 0.55, 95% CI 0.26 to 1.17; p = 0.12). Rates of amenorrhoea where much higher in those allocated UPA compared with LNG-IUS at each time point (3 months: 56% vs. 5%, adjusted OR 29.3, 95% CI 7.37 to 116; 6 months 53% vs. 10%, adjusted OR 11.7 95% CI 3.78 to 36.0; 12 months: 64% vs. 25%, adjusted OR 7.12, 95% CI 2.29 to 22.2). There was no evidence of a difference in the other patient-reported outcomes although there was considerable uncertainty. In those with uterine fibroids, there were no changes in fibroid or uterine volume in either treatment group at 12 months. On endometrial biopsy, seven participants (8%) had evidence of progesterone receptor modulator associated endometrial changes (PAEC) at 12 months, although none was observed at a further 6 months post treatment; there were no cases of endometrial malignancy. Rates of serious adverse events were low, and no patients required admission to hospital for management of deranged liver function tests due to UPA use. Mechanism of action study Effects of UPA administration on the uterus: UPA produced a reduction in cell proliferation in the endometrium, as well as alteration of other local endometrial cellular markers (steroid receptor and steroid metabolising enzyme expression) creating a local endometrial oestrogenic environment. The effects on endometrial cellular markers were reversed upon withdrawal of UPA treatment. Stereological analysis in 19 patients showed that UPA did not produce a reduction in the volume of the uterus, irrespective of coexisting fibroids or adenomyosis. DCE-MRI in 15 patients showed that UPA appears not to have an effect on uterine blood flow. If adenomyosis was present in the uterus, there was a significant increase in plasma volume in the endometrium. However, one of the five women with adenomyosis also had fibroids. Effects of UPA administration on uterine fibroids: DCE-MRI studies showed that UPA produced an average reduction in plasma volume in 11 fibroids, which may be interpreted as being due to a reduction in extracellular matrix components. This finding was not supported by stereological analysis, which failed to show a reduction in the total volume of fibroids in eight patients. However, it should be noted that the number of subjects studied is small. Conclusions Both UPA and LNG-IUS alleviated the adverse impact of heavy menstrual bleeding on quality of life but we found no evidence of a difference between groups over 12 months. UPA was evidently superior to LNG-IUS in terms of inducing amenorrhoea. We observed no difference in reduction in the volume of the uterus, whether or not fibroids were present and no difference in change in the volume of fibroids was observed. Analysis of selected markers of endometrial cellular function demonstrated UPA modulation of the progesterone receptor, resulting in molecular and cellular alteration in steroid receptors within the endometrium, consistent with the development of a local (endometrial) oestrogenic microenvironment. Despite this, there is no evidence of pathological endometrial changes. We demonstrated that alteration in the endometrial microenvironment reverses on cessation of UPA treatment, a key factor for a medical treatment of HMB, particularly for those who wish to preserve fertility. UPA now has restricted availability due to concerns regarding hepatotoxicity. Findings from this study may offer insights into mechanism of action of other SPRM class members. New, effective and acceptable oral medical treatment options are needed to address an important unmet clinical need. Recommendations for research Further studies of medical treatments for HMB Developing other SPRMs, not associated with DILI Other hormonal/non-hormonal medical treatments for HMB Patient populations that encompass both the symptoms of HMB and underlying aetiologies, including structurally normal uterus, adenomyosis and small fibroids Study design with outcome measures impact on menstrual bleeding pattern, pelvic pain and impact on haemoglobin and iron-deficiency, as well as quality of life Qualitative studies to determine what are the most important outcomes to women who suffer HMB Study registration This trial is registered as ISRCTN 20426843. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation Programme and will be published in full in Efficacy and Mechanism Evaluation; Vol. 10, No. 8. See the NIHR Journals Library website for further project information.

Background: To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. Methods: This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (Ktrans, Kep and Ve) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive). Results: The mean values of Ktrans and Ve in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in Ktrans skewness, Ktrans entropy, Ktrans kurtosis, and Ve maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The Ktrans kurtosis and Ktrans entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores. Conclusions: Histogram Analysis of DCE-MRI Ktrans maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores. [ABSTRACT FROM AUTHOR]

To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics for glioma grading on a point-to-point basis. Forty patients with treatment-naïve glioma underwent DCE-MR examination and stereotactic biopsy. DCE-derived parameters including endothelial transfer constant (K trans ), volume of extravascular-extracellular space (v e), fractional plasma volume (f p v), and reflux transfer rate (k ep) were measured within ROIs on DCE maps accurately matched with biopsies used for histologic grades diagnosis. Differences in parameters between grades were evaluated by Kruskal-Wallis tests. Diagnostic accuracy of each parameter and their combination was assessed using receiver operating characteristic curve. Eighty-four independent biopsy samples from 40 patients were analyzed in our study. Significant statistical differences in K trans and v e were observed between grades except v e between grade 2 and 3. K trans showed good to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (area under the curve = 0.802, 0.801 and 0.971, respectively). V e indicated good accuracy in discriminating grade 3 from 4 and 2 from 4 (AUC = 0.874 and 0.899, respectively). The combined parameter demonstrated fair to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (AUC = 0.794, 0.899 and 0.982, respectively). Our study had identified K trans , v e and the combination of parameters to be an accurate predictor for grading glioma. [ABSTRACT FROM AUTHOR]

To analyse the application value of dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) based on computer semi‐automatic segmentation (CSS) algorithm in tumour histological grading of breast cancer patients, the CSS algorithm of DCE‐MRI breast image was established based on Canny edge detection operator and dynamic binarization (DB) algorithm to compare with the fuzzy‐c‐means (FCM) algorithm based on FCM clustering and wavelet transform (WT) algorithm. Besides, CSS was applied to DCE‐MRI image diagnosis in 121 breast cancer patients, who were then classified as grade I, II, and III according to the 2019 edition of the World Health Organization Histological Grading Criteria for Breast Tumors. The results showed that the false positive rate (FPR) of CSS was sharply lower than that of FCM and WT, while the true positive rate (TPR) of CSS increased greatly in contrast to FCM and WT, indicating that there were statistically significant (p < 0.05). The area under the curve (AUC) predicted by FCM, WT, and CSS for histological classification were 0.818, 0.801, and 0.924, respectively. The fractional anisotropy (FA) value of patients with grade III was less than that of patients with grade I and II, while the apparent diffusion coefficient (ADC) value of patients with grade III was greater than that of patients with grade I and II (p < 0.05). The tumour diameter, number of lesions, and number of lymph node metastasis of patients with grade III increased markedly compared to patients with grade I and II (p < 0.05). To sum up, DCE‐MRI image segmentation results based on CSS were superior to FCM and WT in the evaluation of breast tumour histological grading. What's more, there were substantial differences in the obtained tumour diameter, FA value, ADC value, and number of lymph node metastasis based on DCE‐MRI of patients with grade I, II, and III, which could be applied to predict the histological grading of patients with breast cancer. [ABSTRACT FROM AUTHOR]

Purpose: To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement. Methods: Patients with endometrial cancers diagnosed between 2018–2020 and having received both MRI and surgical staging were retrospectively enrolled. Patients were characterized according to histology, tumor size, FIGO stage (MRI and surgical stage), and functional MRI parameters (DCE and DWI/ADC). Statistical analysis was performed to determine if an association could be identified between ADC variables and histology grade. Secondarily, we assessed the degree of agreement between the MRI and surgical stages according to the FIGO classification. Results: The cohort included 45 women with endometrial cancer. Quantitative analysis of ADC variables did not find a statistically significant association with histological tumor grades. DCE showed higher sensitivity than DWI/ADC in the assessment of myometrial invasion (85.00% versus 65.00%) with the same specificity (80.00%). A good agreement between MRI and histopathology for the FIGO stage was found (kappa of 0.72, p < 0.01). Differences in staging between MRI and surgery were detected in eight cases, which could not be justified by the interval between MRI and surgery. Conclusions. ADC values were not useful for predicting endometrial cancer grade, despite the good agreement between MRI interpretation and histopathology of endometrial cancer staging at our center. [ABSTRACT FROM AUTHOR]

BackgroundCortical bone porosity is a major determinant of bone strength. Despite the biomechanical importance of cortical bone porosity, the biological drivers of cortical porosity are unknown. The content of cortical pore space can indicate pore expansion mechanisms; both of the primary components of pore space, vessels and adipocytes, have been implicated in pore expansion. Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in vessel detection in cardiovascular studies, but has not been applied to visualize vessels within cortical bone. In this study, we have developed a multimodal DCE-MRI and high resolution peripheral QCT (HR-pQCT) acquisition and image processing pipeline to detect vessel-filled cortical bone pores.MethodsFor this in vivo human study, 19 volunteers (10 males and 9 females; mean age =63±5) were recruited. Both distal and ultra-distal regions of the non-dominant tibia were imaged by HR-pQCT (82 µm nominal resolution) for bone structure segmentation and by 3T DCE-MRI (Gadavist; 9 min scan time; temporal resolution =30 sec; voxel size 230×230×500 µm3) for vessel visualization. The DCE-MRI was registered to the HR-pQCT volume and the voxels within the MRI cortical bone region were extracted. Features of the DCE data were calculated and voxels were categorized by a 2-stage hierarchical kmeans clustering algorithm to determine which voxels represent vessels. Vessel volume fraction (volume ratio of vessels to cortical bone), vessel density (average vessel count per cortical bone volume), and average vessel volume (mean volume of vessels) were calculated to quantify the status of vessel-filled pores in cortical bone. To examine spatial resolution and perform validation, a virtual phantom with 5 channel sizes and an applied pseudo enhancement curve was processed through the proposed image processing pipeline. Overlap volume ratio and Dice coefficient was calculated to measure the similarity between the detected vessel map and ground truth.ResultsIn the human study, mean vessel volume fraction was 2.2%±1.0%, mean vessel density was 0.68±0.27 vessel/mm3, and mean average vessel volume was 0.032±0.012 mm3/vessel. Signal intensity for detected vessel voxels increased during the scan, while signal for non-vessel voxels within pores did not enhance. In the validation phantom, channels with diameter 250 µm or greater were detected successfully, with volume ratio equal to 1 and Dice coefficient above 0.6. Both statistics decreased dramatically for channel sizes less than 250 µm.ConclusionsWe have a developed a multi-modal image acquisition and processing pipeline that successfully detects vessels within cortical bone pores. The performance of this technique degrades for vessel diameters below the in-plane spatial resolution of the DCE-MRI acquisition. This approach can be applied to investigate the biological systems associated with cortical pore expansion.

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast–enhanced magnetic resonance imaging (DCE–MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann–Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_ADC, model_IVIM, model_DCE, model_DCE+ADC, and model_DCE+IVIM). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_DCE+IVIM showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828–0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_ADC (p = 0.014) and model_IVIM (p = 0.033). The model_ADC had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672–0.846) but was not significantly different from model_IVIM (p = 0.168). The united quantitative model with DCE–MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated.

Abstract Background Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients. Methods A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K trans, V e, K ep) and semiquantitative DCE-MRI parameters (W− in, W− out, TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS). Results LVI-positive patients had a higher K ep value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K ep value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K ep yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence. Conclusion The quantitative parameter of K ep could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS.

Obstructive sleep apnea (OSA) is highly prevalent but easily undiagnosed and is an independent risk factor for cognitive impairment. However, it remains unclear how OSA is linked to cognitive impairment. In the present study, we found the correlation between morphological changes of perivascular spaces (PVSs) and cognitive impairment in OSA patients. Moreover, we developed a novel set of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods to evaluate the fluid dynamics of glymphatic drainage system. We found that the inflow and outflow parameters of the glymphatic drainage system in patients with OSA were obviously changed, indicating impairment of glymphatic drainage due to excessive perfusion accompanied with deficient drainage in OSA patients. Moreover, parameters of the outflow were associated with the degree of cognitive impairment, as well as the hypoxia level. In addition, continuous positive airway pressure (CPAP) enhances performance of the glymphatic drainage system after 1 month treatment in OSA patients. We proposed that ventilation improvement might be a new strategy to ameliorate the impaired drainage of glymphatic drainage system due to OSA-induced chronic intermittent hypoxia, and consequently improved the cognitive decline. [ABSTRACT FROM AUTHOR]

Accurate prediction of pathologic complete response to neoadjuvant chemotherapy non-invasively before treatment via dynamic contrast-enhanced magnetic resonance imaging is vital for developing a personalized therapy strategy. However, the application of deep learning in this domain is characterized by its black-box nature, largely relying on post-hoc analysis to interpret final decision-making. This reliance results in a lack of self-interpretability in the operational mechanisms of feature extraction, feature fusion, and decision-making. Moreover, these models have demonstrated unsatisfactory prediction performance due to insufficient feature modeling. To address these issues, we propose a self-interpretable deep learning network that can provide the intrinsic interpretability of feature extraction, multi-scale feature fusion, and final prediction. First, the interpretable perception module is designed to extract features both effectively and interpretably. Furthermore, the interpretable adaptive multi-scale feature fusion module is proposed to fuse multi-scale features. Finally, an end-to-end self-interpretable deep learning network is presented to predict pathologic complete response with self-interpretability. Validated on a multi-center pre-treatment dynamic contrast-enhanced magnetic resonance imaging dataset, our self-interpretable deep learning network outperforms state-of-the-art methods in both prediction performance and self-interpretability, improving the area under the receiver operating characteristic curve by at least 4.81% while providing both qualitative and quantitative self-interpretability. Our study demonstrates that our proposed self-interpretable deep learning network can extract key information from pre-treatment breast dynamic contrast-enhanced magnetic resonance imaging while enhancing both the prediction performance and the transparency of the model, thereby improving its trustworthiness in clinical settings. [Display omitted] • IPM improves the self-interpretability of feature extraction process. • IFFM adaptively interprets the process of multi-scale feature fusion. • Our SIDLN provides precise and self-interpretable pCR prediction to NAC. • The interpretability of SIDLN surpasses that of the existing post-hoc methods. • Our methods enhance the trustworthiness of decision-making in medicine. [ABSTRACT FROM AUTHOR]

Blood-brain barrier (BBB) dysfunction has been implicated in ischemic risk following aneurysmal subarachnoid hemorrhage (aSAH), but never directly imaged. We prospectively examined whether post-bleed day 4 dynamic contrast-enhanced magnetic resonance (DCE-MR) BBB permeability imaging could predict development of delayed cerebral ischemia (DCI). Global MR-derived BBB permeability ( Ktrans) was significantly higher in aSAH patients who subsequently developed DCI (five patients; 2.28 ± 0.09 × 10-3 min-1) compared to those who experienced radiographic vasospasm only (three patients; 1.85 ± 0.12 × 10-3 min-1; p  2 × 10-3 min-1 predicted development of DCI (AUC = 0.98, 95% CI: 0.93-1). Global BBB dysfunction following aSAH is detectable with DCE-MR and predictive of ischemic risk.