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Objective: Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers. Methods: A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest. Results: The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88–0.97), better than CSI clinical predictor of diameter (AUC-ROC: 0.75), length (AUC-ROC: 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC: 0.70–0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI. Conclusions: The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters. [ABSTRACT FROM AUTHOR]

Background: MR scans used in radiotherapy can be partially truncated due to the limited field of view (FOV), affecting dose calculation accuracy in MR‐based radiation treatment planning. Purpose: We proposed a novel Compensation‐cycleGAN (Comp‐cycleGAN) by modifying the cycle‐consistent generative adversarial network (cycleGAN), to simultaneously create synthetic CT (sCT) images and compensate the missing anatomy from the truncated MR images. Methods: Computed tomography (CT) and T1 MR images with complete anatomy of 79 head‐and‐neck patients were used for this study. The original MR images were manually cropped 10–25 mm off at the posterior head to simulate clinically truncated MR images. Fifteen patients were randomly chosen for testing and the rest of the patients were used for model training and validation. Both the truncated and original MR images were used in the Comp‐cycleGAN training stage, which enables the model to compensate for the missing anatomy by learning the relationship between the truncation and known structures. After the model was trained, sCT images with complete anatomy can be generated by feeding only the truncated MR images into the model. In addition, the external body contours acquired from the CT images with full anatomy could be an optional input for the proposed method to leverage the additional information of the actual body shape for each test patient. The mean absolute error (MAE) of Hounsfield units (HU), peak signal‐to‐noise ratio (PSNR), and structural similarity index (SSIM) were calculated between sCT and real CT images to quantify the overall sCT performance. To further evaluate the shape accuracy, we generated the external body contours for sCT and original MR images with full anatomy. The Dice similarity coefficient (DSC) and mean surface distance (MSD) were calculated between the body contours of sCT and original MR images for the truncation region to assess the anatomy compensation accuracy. Results: The average MAE, PSNR, and SSIM calculated over test patients were 93.1 HU/91.3 HU, 26.5 dB/27.4 dB, and 0.94/0.94 for the proposed Comp‐cycleGAN models trained without/with body‐contour information, respectively. These results were comparable with those obtained from the cycleGAN model which is trained and tested on full‐anatomy MR images, indicating the high quality of the sCT generated from truncated MR images by the proposed method. Within the truncated region, the mean DSC and MSD were 0.85/0.89 and 1.3/0.7 mm for the proposed Comp‐cycleGAN models trained without/with body contour information, demonstrating good performance in compensating the truncated anatomy. Conclusions: We developed a novel Comp‐cycleGAN model that can effectively create sCT with complete anatomy compensation from truncated MR images, which could potentially benefit the MRI‐based treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: To compare the clinically significant prostate cancer (csPCa) detection rate between diffusion-weighted imaging (DWI) 4 and DWI 3 with positive dynamic contrast-enhanced (DCE) (hereinafter called 'DWI 3/DCE+') lesions in the peripheral zone (PZ) and to explore the diagnostic performance of targeted biopsy (TB) or systematic biopsy (SB) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4 lesions. Methods: We retrospectively enrolled 206 patients who underwent multiparametric magnetic resonance imaging and had at least one PI-RADS 4 lesion in the PZ. All patients subsequently underwent combined magnetic resonance imaging/ultrasound fusion-guided TB and ultrasound-guided 12-core SB. The chi-square test was used to compare the csPCa detection rates between DWI 4 and DWI 3/DCE+ lesions. Based on the TB + SB results as a standard reference, we analyzed the sensitivity, negative predictive value, and diagnostic accuracy of TB alone or SB alone. Results: Patients with DWI 4 lesions had higher csPCa detection rate than those with DWI 3/DCE+ lesions when using TB + SB, TB, and SB, and the differences were significant for TB + SB (72.22 vs. 54.84%, p = 0.015) or SB (65.97 vs. 46.77%, p = 0.010). For DWI 3/DCE+ patients whose prostate-specific antigen levels ranged from 4 to 10 ng/mL, TB alone showed the highest negative predictive value (95% Cl 78.12–100). Conclusions: DWI 4 tends to have worse results than DWI 3/DCE+. TB has great diagnostic performances in DWI 3/DCE+ patients, especially for those prostate-specific antigen ranging from 4 to 10 ng/mL. [ABSTRACT FROM AUTHOR]

Cystic lesions are common lesions of the sellar region with various pathological types, including pituitary apoplexy, Rathke's cleft cyst, cystic craniopharyngioma, etc. Suggested surgical approaches are not unique when dealing with different cystic lesions. However, cystic lesions with different pathological types were hard to differentiate on MRI with the naked eye by doctors. This study aimed to distinguish different pathological types of cystic lesions in the sellar region using preoperative magnetic resonance imaging (MRI). Radiomics and deep learning approaches were used to extract features from gadolinium-enhanced MRIs of 399 patients enrolled at Peking Union Medical College Hospital over the past 15 years. Paired imaging differentiations were performed on four subtypes, including pituitary apoplexy, cystic pituitary adenoma (cysticA), Rathke's cleft cyst, and cystic craniopharyngioma. Results showed that the model achieved an average AUC value of 0.7685. The model based on a support vector machine could distinguish cystic craniopharyngioma from Rathke's cleft cyst with the highest AUC value of 0.8584. However, distinguishing cystic apoplexy from pituitary apoplexy was difficult and almost unclassifiable with any algorithms on any feature set, with the AUC value being only 0.6641. Finally, the proposed methods achieved an average Accuracy of 0.7532, which outperformed the traditional clinical knowledge-based method by about 8%. Therefore, in this study, we first fill the gap in the existing literature and provide a non-invasive method for accurately differentiating between these lesions, which could improve preoperative diagnosis accuracy and help to make surgery plans in clinical work. [ABSTRACT FROM AUTHOR]

Purpose: To investigate potential findings associated with cytokeratin 19 (CK19)-positive HCC, with special emphasis on MR texture analysis.Materials and Methods: Forty-eight patients with CK19-negative HCC and 38 patients with CK19-positive were retrospectively evaluated by texture analysis based on conventional MRI. Clinicalpathological characteristics, conventional MR imaging findings, and the MR texture analysis contained of 2415 texture features in the seven conventional sequences were compared. Significant features for differentiating were identified by univariate and multivariate analyses. Receiver operating characteristic analyses of the significant findings were performed and compared to evaluate their diagnostic performance.Results: There was no significant difference between the top1 texture feature (three-dimensional standard deviation separation of intensity on T2-weighted original images, abbreviated as: StdSeparation 3D) and the combined top1-6 feature in identifying CK19-positive HCC(P = 0.660). Univariate and multivariate analyses indicated that serum alpha-fetoprotein (AFP) level ≥400 ng/mL(P = 0.013), arterial rim enhancement(P = 0.005), and StdSeparation 3D texture character(P = 0.002) were independent variables associated with CK19-positive HCCs. The combination of the three indices showed a better performance than AFP level(P = 0.0028), arterial rim enhancement(P < 0.0001), and their combination(P = 0.0098); while no significantly better than the StdSeparation 3D texture character alone(P = 0.0788). An acceptable discrimination(AUC = 0.765) with both sensitivity and specificity greater than 75% was achieved for StdSeparation 3D texture character.Conclusion: Serum AFP level ≥400 ng/mL, arterial rim enhancement, and the StdSeparation 3D texture character were independently associated with CK19-positive HCC. The StdSeparation 3D texture character may be a reliable imaging biomarker which can improve the diagnostic performance. [ABSTRACT FROM AUTHOR]

Abstract Objectives To evaluate the diagnostic value of liver extracellular volume (ECV liver) measurement by equilibrium MR in staging liver fibrosis in chronic hepatitis B (CHB) patients, and to compare its performance with serum fibrosis indices. Materials and methods 91 CHB patients were included and underwent gadopentetate dimeglumine-enhanced MRI with T1 mapping sequence before and 15-min after contrast. ECV liver , aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis index based on the four factors (FIB-4) were calculated and compared between fibrosis subgroups, and the correlations between the three indices and fibrosis stage or inflammatory activity were measured by Spearman correlation analysis and stepwise multiple regression analysis. Diagnostic performance in evaluating liver fibrosis stage was assessed and compared using receiver operating characteristic analysis. Results Interobserver agreement showed an excellent interclass correlation coefficient of 0.895 for ECV liver. ECV liver , APRI and FIB-4 were different between fibrosis stages as a whole (F / H = 18.44–24.36, P ≤ 0.001). ECV liver had the strongest correlation with fibrosis stage (r = 0.727, P < 0.001), while APRI and FIB-4 had weak correlations (r = 0.466 and 0.440, P < 0.001). Multivariate analysis showed that only ECV liver was independently correlated with fibrosis stage (P < 0.001). The fibrosis stage was the only independent factor correlated with ECV liver comparing to inflammatory activity (P < 0.001). AUCs of ECV liver were larger than both APRI and FIB-4 in fibrosis staging, with significant differences in the diagnosis of advanced fibrosis (≥F3) and cirrhosis (F4) (P = 0.0024 to 0.0049). Conclusion MR ECV liver provides a promising noninvasive tool in staging liver fibrosis for CHB patients, superior to the fibrosis indices of APRI and FIB-4. [ABSTRACT FROM AUTHOR]

Objectives: To explore the diagnostic performance of targeted biopsy (TB) combined with regional systematic biopsy (RSB) in patients with different Prostate Imaging Reporting and Data System (PI-RADS) and histologic zones for prostate lesions. Methods: This retrospective study included 1301 patients who underwent multiparametric MRI followed by combined MRI/US fusion-guided TB+systematic biopsy (SB) between January 2019 and October 2022. RSB was defined as the four perilesional SB cores adjacent to an MRI-positive lesion. Cancer detection rates were calculated for TB + SB, TB, SB, and TB + RSB, while the McNemar test was utilized for multiple comparisons among them. Subgroup analyses were performed based on different Pl-RADS and histologic zones. Results: Of 1301 included participants (median age, 68 years; interquartile range, 63–74 years), 16,104 total biopsy cores were performed. TB + RSB detected clinically significant prostate cancer in 70.9% (922/1301) of patients, which was significantly higher than TB (67.4%, p < 0.001) or SB (67.5%, p < 0.001) but similar to TB + SB (71.0%, p = 0.50). Compared with TB + SB, TB + RSB required fewer median biopsy cores (6.3 vs. 12.4, p < 0.001) and had a higher proportion of positive cores (56.3% vs. 39.0%, p < 0.001). Subgroup analysis showed that TB had outstanding sensitivity for detecting PI-RADS 5 lesions in the PZ. Conclusions: Compared with TB + SB, TB + RSB achieved a similar clinically significant prostate cancer detection rate while requiring fewer biopsy cores and exhibiting higher diagnostic efficiency. Critical relevance statement: For MRI-positive prostate lesions, targeted biopsy combined with regional systematic biopsy could serve as an alternative diagnostic approach to targeted biopsy combined with systematic biopsy. Key Points: The scheme of prostate biopsy needs to be optimized. Regional systematic biopsy decreases the total number of cores taken. Targeted biopsies combined with regional systematic biopsies improve prostate diagnostic efficiency. [ABSTRACT FROM AUTHOR]

Purpose: To investigate and compare the diagnostic value of diffusion kurtosis imaging (DKI) with diffusion-weighted imaging (DWI) in assessing and quantifying hepatic fibrosis. Methods: Thirty rats were divided into the control group ( n = 6) and the fibrosis experimental groups ( n = 6 per group) with CCl administration for 2, 4, 6, and 8 weeks. Liver fibrosis stage (S) and necroinflammatory activity grade (G) were histopathologically determined. DKI and DWI were performed; mean apparent diffusion (MD), mean kurtosis (MK), and apparent diffusion coefficient (ADC) values were calculated. DKI parameters were compared with ADC values according to G/S scores. Results: Strong inverse correlations were found between the degree of fibrosis and both MD and ADC ( r = −0.840 and r = −0.760), while only weak correlation existed in MK ( r = 0.405). ROC analyses demonstrated the AUC in MD, MK, and ADC of 0.862, 0.684, 0.817 for identifying mild and severe fibrosis, and 0.757, 0.675, 0.733 for non-cirrhosis and cirrhosis, respectively. The degree of fibrosis was significantly correlated with α-smooth muscle actin (α-SMA) ( P < 0.0001); α-SMA had strong inverse correlation with MD ( r = −0.723), moderate inverse correlation with ADC ( r = −0.613), and very weak correlation with MK ( r = 0.175). Additionally, MD was strongly correlated with the necroinflammatory activity ( r = −0.758), ADC was moderately correlated ( r = −0.492), and MK was weakly correlated ( r = 0.254). Conclusion: DKI may provide added information and serve as a valuable tool for the characterization and surveillance of liver fibrosis in a non-invasive manner. [ABSTRACT FROM AUTHOR]

Background: Although European Society of Urogenital Radiology proposed the potential of multiparametric magnetic resonance imaging (MP-MRI) as a tool in the diagnostic pathway for prostate cancer (PCa) and published a unified scoring system named Prostate Imaging Reporting and Data System (PI-RADS version 1), these still need to be validated by real-life studies. Objective: To evaluate the role of MP-MRI in detection and prediction of PCa. Methods: Patients with clinical suspicion of PCa who underwent prebiopsy MP-MRI from 2002 to 2009 were recruited. MP-MRI results were retrospectively assigned as overall scores using PI-RADS by two radiologists. Patients were followed and the end point was the diagnosis of PCa. Receiver operating characteristics (ROC) curve was performed to test diagnostic efficacy of MP-MRI, under results of biopsy within three months. The cox proportional hazards model was used to identify independent variables for the detection of PCa. Results: Finally, 1113 of the 1806 enrolled patients were included for analysis. The median follow-up was 56.0 months (1–137 mo). For 582 patients biopsied within three months, area under the curve for the detection of PCa with MP-MRI was 0.88 (95% confidence interval [CI], 0.75–1.00) in group of baseline prostate specific antigen (PSA) 0.01–4.00 ng/ml (n = 31), 0.90 (95% CI, 0.84–0.95) in PSA 4.01–10.00 ng/ml (n = 142), and 0.91 (95% CI, 0.87–0.94) in PSA >10.00 ng/ml (n = 409), respectively. In the cox model adjusted for age and baseline PSA level, for the detection rate of PCa, compared with PI-RADS 1–2 (reference), the hazard ratio was 6.43 (95% CI, 4.29–9.65) for PI-RADS 3, 18.58 (95% CI, 13.36–25.84) for PI-RADS 4–5 (p < 0.001). Conclusions: Prebiopsy MP-MRI with PI-RADS is demonstrated as a valuable diagnostic and predictive tool for PCa. [ABSTRACT FROM AUTHOR]

Aims: The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)‐based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI‐based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual‐level morphological networks. Background: There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual‐level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders. Conclusion: This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions. [ABSTRACT FROM AUTHOR]

Background: Accurate segmentation of pelvic bones is an initial step to achieve accurate detection and localisation of pelvic bone metastases. This study presents a deep learning-based approach for automated segmentation of normal pelvic bony structures in multiparametric magnetic resonance imaging (mpMRI) using a 3D convolutional neural network (CNN). Methods: This retrospective study included 264 pelvic mpMRI data obtained between 2018 and 2019. The manual annotations of pelvic bony structures (which included lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis) on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images were used to create reference standards. A 3D U-Net CNN was employed for automatic pelvic bone segmentation. Additionally, 60 mpMRI data from 2020 were included and used to evaluate the model externally. Results: The CNN achieved a high Dice similarity coefficient (DSC) average in both testing (0.80 [DWI images] and 0.85 [ADC images]) and external (0.79 [DWI images] and 0.84 [ADC images]) validation sets. Pelvic bone volumes measured with manual and CNN-predicted segmentations were highly correlated (R2 value of 0.84–0.97) and in close agreement (mean bias of 2.6–4.5 cm3). A SCORE system was designed to qualitatively evaluate the model for which both testing and external validation sets achieved high scores in terms of both qualitative evaluation and concordance between two readers (ICC = 0.904; 95% confidence interval: 0.871–0.929). Conclusions: A deep learning-based method can achieve automated pelvic bone segmentation on DWI and ADC images with suitable quantitative and qualitative performance. [ABSTRACT FROM AUTHOR]

Lateral ankle sprains (LAS) might lead to joint sensory deafferentation, which induces maladaptive neuroplasticity, especially the morphological atrophy of the cerebellar vermis. However, longitudinal evidence on the causality of injury and neural differences is still lacking. To this end, this study aimed to determine whether the morphology of the central nervous system would be altered before and after ligament transection in LAS mouse models. A total of 40 C57BL/6 mice were randomly divided among the LAS, Sham and Blank groups. We repeatedly performed the balance beam test and neural voxel‐based morphometry (VBM) measurements using an 11.7 T magnetic resonance imaging before and 2 months after the surgery. The results showed that for balance outcomes, the LAS group had a significantly longer time and more slips of the balance beam tests compared with the Sham and Blank groups at 2 months after surgery, with no significant difference among the three groups before surgery. Regarding the VBM analysis, the LAS group showed significantly lower VBM values in the central lobule III of the cerebellar vermis and medial amygdalar nucleus (MEA) compared with the Sham and Blank groups after surgery, with no significant difference among the three groups before surgery. In conclusion, lateral ligament injuries might lead to morphological atrophy of the cerebellar vermis in animal models, which might pave the way for the pathological process of ankle instability after LAS. [ABSTRACT FROM AUTHOR]

Purpose: Joint deafferentation after post-ankle sprain ligament healing can disrupt sensory input from the ankle and induce maladaptive neuroplasticity, especially in the cerebellum. This study aimed to determine whether the regional homogeneity of intrinsic cerebellar activity differs between patients with ankle instability and healthy controls without a history of ankle injury. Methods: The current study used a primary data set of 18 patients and 22 healthy controls and an external UK Biobank data set of 16 patients with ankle instability and 69 healthy controls for a cross-database, cross-sectional investigation. All participants underwent resting-state functional magnetic resonance imaging to calculate their regional homogeneity (ReHo) value. Between-group comparisons of the sensorimotor-related subregions of the cerebellum were first performed in the primary data set to identify low cerebellar ReHo in patients with multiple comparison corrections, and the surviving subregions were then externally validated in the UK Biobank data set. Correlation analyses between the ReHo values and clinical features were also performed. Results: The ReHo value of cerebellar lobule VIIIb was significantly lower in the ankle instability group than in the controls (0.170 ± 0.016 vs 0.184 ± 0.019 in the primary data set, 0.157 ± 0.026 vs 0.180 ± 0.042 in the UK Biobank data set). The ReHo values of this subregion showed a significant positive correlation with the Cumberland Ankle Instability Tool scores in the ankle instability group (r = 0.553, P -corrected = 0.0348). Conclusions: Patients with ankle instability had lower intraregional coherence in cerebellar lobule VIIIb than that of controls, which was also positively correlated with the intensity of self-reported ankle instability. [ABSTRACT FROM AUTHOR]

Objective. To develop and internally validate nomograms based on multiparametric magnetic resonance imaging (mpMRI) to predict prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in patients with a previous negative prostate biopsy. Materials and Methods. The clinicopathological parameters of 231 patients who underwent a repeat systematic prostate biopsy and mpMRI were reviewed. Based on Prostate Imaging and Reporting Data System, the mpMRI results were assigned into three groups: Groups “negative,” “suspicious,” and “positive.” Two clinical nomograms for predicting the probabilities of PCa and csPCa were constructed. The performances of nomograms were assessed using area under the receiver operating characteristic curves (AUCs), calibrations, and decision curve analysis. Results. The median PSA was 15.03 ng/ml and abnormal DRE was presented in 14.3% of patients in the entire cohort. PCa was detected in 75 patients (32.5%), and 59 (25.5%) were diagnosed with csPCa. In multivariate analysis, age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE), and mpMRI finding were significantly independent predictors for PCa and csPCa (all p < 0.01). Of those patients diagnosed with PCa or csPCa, 20/75 (26.7%) and 18/59 (30.5%) had abnormal DRE finding, respectively. Two mpMRI-based nomograms with super predictive accuracy were constructed (AUCs = 0.878 and 0.927, p < 0.001), and both exhibited excellent calibration. Decision curve analysis also demonstrated a high net benefit across a wide range of probability thresholds. Conclusion. mpMRI combined with age, PSA, PV, and DRE can help predict the probability of PCa and csPCa in patients who underwent a repeat systematic prostate biopsy after a previous negative biopsy. The two nomograms may aid the decision-making process in men with prior benign histology before the performance of repeat prostate biopsy. [ABSTRACT FROM AUTHOR]

To determine the accuracy, repeatability, and reproducibility of magnetic resonance imaging-based proton density fat fraction (MRI-PDFF) measurements of rotator cuff muscles between two readers and three different scanners. Twenty-seven volunteers underwent serial shoulder MRI examinations of both left and right sides on one 1.5-T MRI scanner and two 3.0-T MRI scanners. Two independent readers measured muscular PDFF of the supraspinatus, infraspinatus/teres minor muscle, and subscapularis. MR spectroscopy-based proton density fat fraction (MRS-PDFF) was regarded as the reference standard for assessing accuracy. A "coffee break" examination method was used to test the repeatability of each scanner. Bland-Altman plots, Pearson correlation, and linear regression analysis were used to assess bias and linearity. The Wilcoxon signed-rank test and Friedman test were applied to evaluate repeatability and reproducibility. MRI-PDFF measurements indicated strong linearity (R2 = 0.749) and small bias (−0.18%) in comparison with the MRS-PDFF measurements. A very strong positive Pearson correlation (r = 0.955–0.986) between the PDFF estimates of the two repeat scans indicated excellent repeatability. The PDFF measurements showed high reproducibility, with a strong positive Pearson correlation (r = 0.668–0.698) and a small mean bias (−0.04 to −0.10%) across different scanners. MRI-PDFF measurements of rotator cuff muscles were highly accurate, repeatable, and reproducible across different readers and scanners, leading us to the conclusion that PDFF can be a reliable and robust quantitative imaging biomarker for longitudinal or multi-center studies. [ABSTRACT FROM AUTHOR]

Objective: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a 4-dimensional (4D) deep learning model based on 3D convolution and convolutional long short-term memory (C-LSTM) for hepatocellular carcinoma (HCC) lesion segmentation. Methods: The proposed deep learning model utilizes 4D information on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) images to assist liver tumor segmentation. Specifically, a shallow U-net based 3D CNN module was designed to extract 3D spatial domain features from each DCE phase, followed by a 4-layer C-LSTM network module for time domain information exploitation. The combined information of multi-phase DCE images and the manner by which tissue imaging features change on multi-contrast images allow the network to more effectively learn the characteristics of HCC, resulting in better segmentation performance. Results: The proposed model achieved a Dice score of 0.825± 0.077, a Hausdorff distance of 12.84± 8.14 mm, and a volume similarity of 0.891± 0.080 for liver tumor segmentation, which outperformed the 3D U-net model, RA-UNet model and other models in the ablation study in both internal and external test sets. Moreover, the performance of the proposed model is comparable to the nnU-Net model, which showed state-of-the-art performance in many segmentation tasks, with significantly reduced prediction time. Conclusion: The proposed 3D convolution and C-LSTM based model can achieve accurate segmentation of HCC lesions. [ABSTRACT FROM AUTHOR]

Creating synthetic CT (sCT) from magnetic resonance (MR) images enables MR-based treatment planning in radiation therapy. However, the MR images used for MR-guided adaptive planning are often truncated in the boundary regions due to the limited field of view and the need for sequence optimization. Consequently, the sCT generated from these truncated MR images lacks complete anatomic information, leading to dose calculation error for MR-based adaptive planning. We propose a novel structure-completion generative adversarial network (SC-GAN) to generate sCT with full anatomic details from the truncated MR images. To enable anatomy compensation, we expand input channels of the CT generator by including a body mask and introduce a truncation loss between sCT and real CT. The body mask for each patient was automatically created from the simulation CT scans and transformed to daily MR images by rigid registration as another input for our SC-GAN in addition to the MR images. The truncation loss was constructed by implementing either an auto-segmentor or an edge detector to penalize the difference in body outlines between sCT and real CT. The experimental results show that our SC-GAN achieved much improved accuracy of sCT generation in both truncated and untruncated regions compared to the original cycleGAN and conditional GAN methods. • An automatic workflow to generate synthetic CT from truncated MR images. • Achieve anatomic compensation using a novel truncation loss function. • Improve synthetic CT image quality in both truncated and non-truncated regions. • Facilitate accurate dose calculation for MR-guided online adaptive planning. [ABSTRACT FROM AUTHOR]

• Modal controllability and synchronizability increase in the internet gaming addicts. • Addiction mediates the relationship between brain controllability and anxiety state. • Regional morphometry is associated with the brain controllability. • Specific regional controllability could deeply affect brain states. Internet gaming addiction (IGD) is a common disease in teenagers which usually reflects the abnormalities in brain function or structure. Several computational models have been applied to investigate the characteristic of IGD brain networks, for instance, the conception of brain controllability. The primary objective of this study was to explore the relationship between brain controllability and IGD related clinical behaviour. A sample of 101 subjects, including 49 IGD patients and 52 normal controls, were recruited to undergo MR T1 and DTI scanning. Specifically, the MR images were used to generate the white matter connectivity matrix and the morphometry similarity network. The morphometry similarity network was then divided into several communities using modular decomposition. After, average controllability, modal controllability and synchronizability were calculated through measuring the adjacency matrix. The results indicated that the IGD group had greater synchronizability and modal controllability compared to that of the control group, and different morphological-based brain communities had different controllability properties. Furthermore, the addiction demonstrated the mediating effects between nodal or modular brain controllability as well as anxiety. In conclusion, brain controllability could be a potential biomarker of IGD. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to investigate the use of MRI in predicting prostate growth and development. Methods: A total of 1,500 healthy male volunteers who underwent MRI of the pelvis were included in this prospective study. Subjects were divided into five groups according to age (group A, 2-5 years; group B, 6-10 years; group C, 11-15 years; group D, 16-20 years; group E, 21-25 years). Total prostate volume (TPV) as well as prostate central zone (CZ) and peripheral zone (PZ) were measured and evaluated on MRI. Data of the different groups were compared using variance analysis, Scheffé's method, Kruskal-Wallis H-test, and Pearson's correlation. Statistical significance was inferred at P < 0.05. Results: In groups A and B, the prostates were barely visible. In group C, although TPV was measured, it was hard to distinguish CZ and PZ. In group D, 136 CZ and PZ were clearly visible. In group E, 377 CZ and PZ were clearly visible on T2-weighted imaging (T2WI). The median TPVs of groups A, B, C, D, and E were 0.00 cm, 0.05 cm, 2.83 cm, 8.32 cm and 11.56 cm, respectively, and the median prostate development scores were 0.08, 0.69, 1.56, 2.38, and 2.74, respectively. Both TPVs and zonal anatomy scores varied significantly among the five groups ( P = 0.000). TPV and zonal anatomy score increased with increasing age. Conclusions: MRI provides a reliable quantitative reference for prostate growth and development. Key Points: • When and how the prostate develops after birth remains unclear. • Prostate volume increases rapidly after the age of 10 years. • MRI provides a reliable objective and quantitative reference for prostate growth and development. [ABSTRACT FROM AUTHOR]

• 'Cold' or 'hot' inflammatory status of glioblastoma plays a key role in immunotherapy. • TAG nanoprobes are with surface modification of ANG to target glioma and phenolic hydroxyl to sense myeloperoxidase. • TAG nanoprobes noninvasively distinguish between the 'hot' and 'cold' status of GBM using MRI. • 'Hot' glioma showed patchy hypointense T 1 signal, while 'cold' glioma showed moderate hyperintensity within the tumor after intravenous administration of TAGs. The 'cold' or 'hot' inflammatory status of glioblastoma (GBM) is closely related to the responsiveness of immunotherapy. Tumor-associated macrophages (TAMs) are the most abundant tumor infiltrating immune cells in the tumor microenvironment (TME) of GBM, which can be classified as pro- (M1-like) and anti-inflammatory (M2-like) phenotype. M1-like TAMs present in large amount in 'hot' TME and indicate better immunotherapeutic outcome, which secrete myeloperoxidase (MPO) to promote inflammation in host defense. Herein, magnetic resonance imaging (MRI) based Tyr-ANG-NaGdF 4 nanoprobes, named as TAGs, with surface modification of ANG to target glioma and phenolic hydroxyl to sense MPO were developed, aiming to noninvasively visualize the 'hot' or 'cold' TME inflammatory status of GBM based on MRI. Rat C6 gliomas, which were histologically confirmed as MPO-enriched 'hot' TME, showed patchy hypointense T 1 signal within the tumor after intravenous administration of TAGs, ascribing to the MPO responsive assembly of the nanoprobes which led to the T 1 signal reverse. On the contrary, mouse GL261 gliomas, which was histologically confirmed as MPO-rare 'cold' TME, presented moderate hyperintensity in T 1 -weighted MRI. Therefore, the MPO responsive nanoprobes present potential means to monitor the TME inflammatory status for GBM patients thus guide clinical treatment. [ABSTRACT FROM AUTHOR]

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (χ 13 , χ 23 and χ 33) and the acquired single-orientation phase. The convolutional neural networks are embedded into the physical model to learn a regularization term containing prior information. χ 33 and phase induced by χ 13 and χ 23 terms were used as the labels for network training. Quantitative evaluation metrics were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Progressive gray matter (GM) atrophy is a hallmark of multiple sclerosis (MS). Cognitive impairment has been observed in 40%–70% of MS patients and has been linked to GM atrophy. In a phase 2 trial of estriol treatment in women with relapsing–remitting MS (RRMS), higher estriol levels correlated with greater improvement on the paced auditory serial addition test (PASAT) and imaging revealed sparing of localized GM in estriol‐treated compared to placebo‐treated patients. To better understand the significance of this GM sparing, the current study explored the relationships between the GM sparing and traditional MRI measures and clinical outcomes. Methods: Sixty‐two estriol‐ and forty‐nine placebo‐treated RRMS patients underwent clinical evaluations and brain MRI. Voxel‐based morphometry (VBM) was used to evaluate voxelwise GM sparing from high‐resolution T1‐weighted scans. Results: A region of treatment‐induced sparing (TIS) was defined as the areas where GM was spared in estriol‐ as compared to placebo‐treated groups, localized primarily within the frontal and parietal cortices. We observed that TIS volume was directly correlated with improvement on the PASAT. Next, a longitudinal cognitive disability‐specific atlas (DSA) was defined by correlating voxelwise GM volumes with PASAT scores, that is, areas where less GM correlated with less improvement in PASAT scores. Finally, overlap between the TIS and the longitudinal cognitive DSA revealed a specific region of cortical GM that was preserved in estriol‐treated subjects that was associated with better performance on the PASAT. Conclusions: Discovery of this region of overlap was biology driven, not based on an a priori structure of interest. It included the medial frontal cortex, an area previously implicated in problem solving and attention. These findings indicate that localized GM sparing during estriol treatment was associated with improvement in cognitive testing, suggesting a clinically relevant, disability‐specific biomarker for clinical trials of candidate neuroprotective treatments in MS. [ABSTRACT FROM AUTHOR]

• Radiomics model was useful in determining muscle invasion of bladder cancer. • Radiomics model was helpful for inexperienced readers determining muscle invasion. • Radiomics model showed no added benefit to VI-RADS for experienced readers. To develop predictive models based on the integration of radiomics with the Vesical Imaging-Reporting and Data System (VI-RADS) for determining muscle invasion of bladder cancer. One hundred ninety-one patients were retrospectively included in this study from January 2015 to March 2022. Of these, 121 data were randomly divided into training and validation sets at a ratio of 7:3. The remaining data (n = 70) served as the independent testing set. The radiomics features were extracted from bladder cancer on high-b-value DWI images. The pipelines of radiomics models were trained in the training set. One optimal model was selected based on the performance in the validation set. Then, the selected model was tested in the independent testing set. Two radiologists evaluated the VI-RADS based on T2WI and DWI. Reader 1 was an experienced reader, and Reader 2 was an inexperienced reader. A clinical-radiomics model was built by integrating the radiomics signature and VI-RADS. The performance was assessed using receiver operating characteristic curve analysis. The histopathological results were used as the standard reference to assess the diagnostic accuracy of muscle invasion. The radiomics model had area under the curve (AUC) values of 0.801, 0.867, and 0.806 in the training, validation, and testing sets, respectively. The VI-RADS scores of Readers 1/2 yielded AUC values of 0.831/0.781, 0.909/0.815, and 0.871/0.776 in the training, validation, and testing sets, respectively. The clinical-radiomics model for Readers 1/2 revealed AUC values of 0.889/0.854, 0.961/0.919, and 0.881/0.844 in the training, validation, and testing sets, respectively. The performance of the clinical-radiomics model was improved compared to the VI-RADS score for inexperienced Reader 2 (P < 0.05). The radiomics model was useful in the diagnosis of muscle invasion of bladder cancer. The clinical-radiomics model integrating radiomics and VI-RADS further improved the performance compared to VI-RADS alone, which was helpful for readers with less diagnostic experience. [ABSTRACT FROM AUTHOR]

Purpose: To exploit the intravoxel incoherent motion (IVIM) diffusion-weighted (DW) MRI when evaluating the therapeutic response of gastrointestinal stromal tumors (GIST) to Imatinib in a mouse model. Materials and Methods: Mice with xenografts bearing cells from the GIST-T1 cell line were randomly divided into a treated group receiving Imatinib and a control group. DWMRI scans with 14 b-values (0–1500 s/mm2) were performed before and after treatment (days 1, 3 and 7). IVIM related parameters perfusion fractions (fp) and perfusion-related diffusion coefficients (D*) and the conventional apparent diffusion coefficients (ADC) were calculated by fitting the DWMRI signal decay. The mean changes from baseline to each post-treatment time point for each measurement (ΔADC, Δfp and ΔD*) were calculated. The differences of mean changes between the two groups were tested for statistical significance. Histopathological analyses including Ki-67, CD31, TUNEL and H&E were conducted in conjunction with the MRI scans. Results: Increases in ADC of the treated group were higher than those of the control group after treatment, whereas statistical significances were not observed. Compared to the control group, D* in the treated group decreased significantly (ΔD*treated = -41%, -49%, and -49% with P = 0.0001, 0.0001 and 0.0001), and fp increased significantly (Δfptreated = 79%, 82% and 110%, with P = 0.001, 0.0001 and P = 0.0007) on days 1, 3 and 7 after treatment. Histopathological analyses demonstrated different tumor tissue characteristics between the treated and control groups. Conclusion: IVIM measurements may serve as more sensitive imaging biomarkers than ADC when assessing GIST response to Imatinib as early as one day after treatment. [ABSTRACT FROM AUTHOR]

Purpose Diffusion kurtosis imaging (DKI) is a MRI technique which can measure alterations in the diffusion of water molecules to reflect tissue changes in both white and grey matter. This study evaluated the potential of DKI for the early diagnosis of radiation-induced temporal lobe changes in the grey and white matter of the temporal lobe in patients with nasopharyngeal carcinoma (NPC). Materials and methods Sixty patients with NPC who had normal MRI brain scans were enrolled and underwent DKI at 1 week (n=20), 6 months (n=20) or 1 year (n=20) after radiotherapy; 20 normal control individuals were also evaluated. Nonlinear fitting routines and equations were used to calculate mean diffusion (MD) and mean kurtosis (MK) and fractional anisotropy (FA). Analysis of variance was used to compare the MK/MD/FA values of white and grey matter between groups. Results Compared to the normal control group, grey and white matter MK values were significantly higher at 1 week after radiotherapy and significantly lower at 6 months and 1 year after radiotherapy in patients with NPC, whereas the grey and white matter MD values were significantly lower at 1 week after radiotherapy and returned to normal by 6 months and 1 year after radiotherapy. Conclusion DKI can be used to detect radiotherapy-induced changes in both the white and grey matter of temporal lobe in patients with NPC. MK and MD values may represent reliable indicators for the early diagnosis of radiation-induced temporal lobe changes in NPC. [ABSTRACT FROM AUTHOR]

Objectives To investigate the efficiency of multiparametric MRI (mpMRI) based on Prostate Imaging Reporting and Data System (PI-RADS) Version 2 (v2) in detecting clinically significant prostate cancer (PCa) and to test the interobserver consistency. Methods Based on PI-RADS v2, two radiologists reviewed the images of 372 patients who underwent prostate biopsy and prebiopsy mpMRI. Results There's significant correlation between higher PI-RADS score and the presence of clinical significant PCa ( P < .001). PI-RADS score 3 was the best cutoff point with sensitivity and specificity over 80%. The diagnostic concordance was moderate (kappa = 0.478). Conclusions PI-RADS v2 demonstrated good accuracy in detecting clinically significant PCa, however the interobserver consistency needs to be improved. [ABSTRACT FROM AUTHOR]

Aim: To evaluate whether some magnetic resonance imaging (MRI) signs suggesting idiopathic intracranial hypertension (IIH) could also be found in intracranial hypertension (IH) due to cerebral venous thrombosis (CVT) and to assess their possible contribution to diagnosing this disorder.Materials and Methods: Thirty-one patients with IH due to CVT were evaluated prospectively using MRI. A group of 33 age- and sex-matched healthy volunteers served as controls. The optic nerve and sheath, pituitary gland, and ventricles were assessed. The prevalence of each imaging feature was compared between the two groups.Results: Optic nerve sheath (ONS) dilatation and decreased pituitary gland height were the most valid signs suggesting IH in CVT patients: sensitivity 70.97% and 87.1%, respectively; specificity 96.97% and 72.73%, respectively; area under the curve 0.840 and 0.809, respectively. The MRI finding that showed the strongest association with IH in CVT patients was ONS dilatation (odds ratio 78.5).Conclusions: The combination of T1-weighted volumetric MRI and magnetic resonance venography could be helpful for diagnosing IH with CVT. Abnormalities of the ONS and the pituitary gland were reliable diagnostic signs for IH due to CVT. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the feasibility of enhanced T2 star-weighted angiography (ESWAN) in differentiating endometrial from non-endometrial cysts. Methods: Forty-nine patients with 60 histopathologically proven ovarian cystic lesions underwent pelvic MRI including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), liver acquisition with volume acceleration, and ESWAN. Ovarian cystic lesions were divided into endometrial cysts (group 1; n = 28), pyosalpinx and hydrosalpinx (group 2; n = 13), and ovarian cystic and cystic-solid tumors (group 3; n = 19). R2* (effective transverse relaxation rate) values were measured and pairwise comparison of the R2* values among the three groups was made using Kruskal-Wallis test. Receiver operating characteristic curves were used to calculate cutoff values and performance of R2* values for distinguishing among groups. T1WI signal intensity and R2* value were also compared using area under curve values. Results: R2* values for group 1 were statistically higher than groups 2 and 3 (15.37, 1.40, and 1.79 Hz, respectively; P < 0.001). The cutoff value for R2* was 7.43 Hz with a sensitivity, specificity, PPV, NPV, and accuracy of 96.43, 87.50, 87.10, 96.55, and 91.67%, respectively. There was no significant difference between the R2* value and T1WI in diagnosing endometrial cysts. Conclusions: The R2* value provides an effective way to discriminate endometrial cysts from other ovarian cystic lesions. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the non-Gaussian water diffusion properties of prostate cancer (PCa) and determine the diagnostic performance of diffusion kurtosis (DK) imaging for distinguishing PCa from benign tissues within the peripheral zone (PZ), and assessing tumor lesions with different Gleason scores. Materials and Methods: Nineteen patients who underwent diffusion weighted (DW) magnetic resonance imaging using multiple b-values and were pathologically confirmed with PCa were enrolled in this study. Apparent diffusion coefficient (ADC) was derived using a monoexponential model, while diffusion coefficient (D) and kurtosis (K) were determined using a DK model. Differences between the ADC, D and K values of benign PZ and PCa, as well as those of tumor lesions with Gleason scores of 6, 7 and ≥8 were assessed. Correlations between parameters D and K in PCa were analyzed using Pearson’s correlation coefficient. ADC, D and K values were correlated with Gleason scores of 6, 7 and ≥8, respectively. Results: ADC and D values were significantly (p <0.001) lower in PCa (0.79±0.14μm2/ms and 1.56±0.23μm2/ms, respectively) compared to benign PZ (1.23±0.19μm2/ms and 2.54±0.24μm2/ms, respectively). K values were significantly (p <0.001) greater in PCa (0.96±0.20) compared to benign PZ (0.59±0.08). D and K showed fewer overlapping values between benign PZ and PCa compared to ADC. There was a strong negative correlation between D and K values in PCa (Pearson correlation coefficient r=−0.729; p <0.001). ADC and K values differed significantly in tumor lesions with Gleason scores of 6, 7 and ≥8 (p <0.001 and p =0.001, respectively), although no significant difference was detected for D values (p =0.325). Significant correlations were found between the ADC value and Gleason score (r=−0.828; p <0.001), as well as the K value and Gleason score (r=0.729; p <0.001). Conclusion: DK model may add value in PCa detection and diagnosis. K potentially offers a new metric for assessment of PCa. [ABSTRACT FROM AUTHOR]

Abstract: Diffusion weighted magnetic resonance imaging (DWI) has been mostly acquired using single-shot echo-planar imaging (ss EPI) to minimize motion induced artifacts. The spatial resolution, however, is inherently limited in ss EPI especially for abdominal imaging, even with the advances in parallel imaging. A novel method of reduced Field of View ss EPI (rFOV ss EPI) has achieved high resolution DWI in human carotid artery, spinal cord with reduced blurring and higher spatial resolution than conventional ss EPI, but it has not been used to pancreas imaging. In the work, comparisons between the full FOV ss-DW EPI and rFOV ss-DW EPI in image qualities and ADC values of pancreatic tumors and normal pancreatic tissues were performed to demonstrate the feasibility of pancreatic high resolution rFOV DWI. There were no significant differences in the mean ADC values between full FOV DWI and rFOV DWI for the 17 subjects using b=600s/mm2 (P=0.962). However, subjective scores of image quality was significantly higher at rFOV ss DWI (P=0.008 and 0.000 for b-value=0s/mm2 and 600s/mm2 respectively). The spatial resolution of DWI for pancreas was increased by a factor of over 2.0 (from almost 3.0mm/pixel to 1.25mm/pixel) using rFOV ss EPI technique. Reduced FOV ss EPI can provide good DW images and is promising to benefit applications for pancreatic diseases. [Copyright &y& Elsevier]

Purpose To identify cerebral radiomic features related to the diagnosis of Internet gaming disorder (IGD) and construct a radiomics-based machine-learning model for IGD diagnosis. Methods A total of 59 treatment-naïve subjects with IGD and 69 age- and sex-matched healthy controls (HCs) were recruited and underwent anatomic and diffusion-tensor magnetic resonance imaging (MRI). The features of the morphometric properties of gray matter and diffusion properties of white matter were extracted for each participant. After excluding the noise feature with single-factor analysis of variance, the remaining 179 features were included in an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power. Random forest classifiers were constructed and evaluated based on the identified features. Results No overall differences in the total brain volume (1,555,295.64 ± 152,316.31 mm3 vs. 154,491.19 ± 151,241.11 mm3), total gray (709,119.83 ± 59,534.46 mm3 vs. 751,018.21 ± 58,611.32 mm3) and white (465,054.49 ± 51,862.65 mm3 vs. 470,600.22 ± 47,006.67 mm3) matter volumes, and subcortical region volume (63,882.71 ± 5110.42 mm3 vs. 64,764.36 ± 4332.33 mm3) between the IGD and HC groups were observed. The mean classification accuracy was 73%. An altered cortical shape in the bilateral fusiform, left rostral middle frontal (rMFG), left cuneus, left parsopercularis (IFG), and regions around the right uncinate fasciculus (UF) and left internal capsule (IC) contributed significantly to group discrimination. Conclusions: Our study found the brain morphology alterations between IGD subjects and HCs through a radiomics-based machine-learning method, which may help revealing underlying IGD-related neurobiology mechanisms. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: The objective of this paper was to investigate the value of apparent diffusion coefficients (ADCs) for differential diagnosis among solid pancreatic masses using respiratory triggered diffusion-weighted MR imaging with inversion-recovery fat-suppression technique (RT-IR-DWI) at 3.0T. Materials and Methods: 20 normal volunteers and 72 patients (Pancreatic ductal adenocarcinoma [PDCA, n=30], mass-forming pancreatitis [MFP, n=15], solid pseudopapillary neoplasm [SPN, n=12], and pancreatic neuroendocrine tumor[PNET, n=15]) underwent RT-IR-DWI (b values: 0 and 600s/mm2) at 3.0T. Results were correlated with histopathologic data and follow-up imaging. ADC values among different types of pancreatic tissue were statistically analyzed and compared. Results: Statistical difference was noticed in ADC values among normal pancreas, MFP, PDCA, SPN and PNET by ANOVA (p<.001). Normal pancreas had the highest ADC value, then followed by PNET, PDCA, MFP and SPN. There was noticeable statistical difference in ADC values among PDCA, MFP and normal pancreas by Least Significant Difference (LSD) (p<.001). ADC of SPN was statistically lower than that of PNET (p=0.1800×10−4), PDCA (p=0.0300×10−4) and normal pancreas (p=0.0007×10−4). ADC of PNET was statistically lower than that of normal pancreas (p=0.0360) and higher than that of MFP (p=9.3000×10−4). Conclusions: ADC measurements using RT-IR-DWI at 3.0T may aid to disclose the histopathological pattern of normal pancreas and solid pancreatic masses, which may be helpful in characterizing solid pancreatic lesions. [Copyright &y& Elsevier]

Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2–S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1–S2 vs. S3–S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1–S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging. [ABSTRACT FROM AUTHOR]

Rationale and objectives: The aim of this study was to investigate the efficacy of magnetic resonance imaging (MRI) with a spoiled gradient-recalled (SPGR) sequence to evaluate early knee cartilage degeneration and the relationship between cartilage and other tissues using a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Materials and methods: Eighty-four patients with knee joint pain were evaluated by X-ray and MRI with an SPGR sequence from June 2015 to December 2016. Joint degeneration was graded by two experienced radiologists using the Kellgren-Lawrence (K-L) grading scale. The modified WORMS was used to evaluate cartilage lesions, bone marrow abnormalities, bone cysts, osteophytes, joint effusion and synovitis. The difference between the WORMS of the SPGR and the T2 sequences evaluated by the Wilcoxon signed-rank test was determined, and the relationships between the WORMS features were evaluated by a Spearman correlation. Results: The modified WORMS for the cartilage lesion evaluation was significantly higher with the SPGR sequence than with the T2 sequence (P < 0.05). The cartilage lesions showed a moderate correlation with osteophytes, synovitis and joint effusion (Rs > 0.40, P < 0.05) and weak correlations with bone marrow abnormalities and bone cysts (Rs < 0.4, P < 0.05). Conclusion: The modified WORMS evaluation using MRI with the SPGR sequence was much better than the normal sequence for early knee osteoarthritis (OA). The cartilage lesions are associated with bone marrow abnormalities and the other features of OA. [ABSTRACT FROM AUTHOR]