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PurposeThe purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa).MethodsThis study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin1, TEmin2, and TE90, respectively). A linear regression model was determined to match the C-maps of TE90 to the reference C-maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrsTE90corr) and uncorrected TE90 (RSIrsTE90) to RSIrs from reference TEmin1 (RSIrsTEmin1) and repeated TEmin2 (RSIrsTEmin2). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC).ResultsScaling factors for C1, C2, C3, and C4 were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non-csPCa cases, the 98th percentile of RSIrsTEmin2 and RSIrsTEmin1 differed by 0.27 ± 0.86SI (mean ± standard deviation), whereas RSIrsTE90 differed from RSIrsTEmin1 by 1.82 ± 1.20SI. After calibration, this bias was reduced to -0.51 ± 1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54 ± 1.98SI between RSIrsTEmin2 and RSIrsTEmin1 and 2.28 ± 2.06SI between RSIrsTE90 and RSIrsTEmin1. After calibration, the mean difference decreased to -1.03SI, a 55% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrsTEmin1 has a sensitivity of 66% and a specificity of 72%.ConclusionsThe proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 72% and 55% for non-csPCa and csPCa, respectively.

Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions. A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings. [ABSTRACT FROM AUTHOR]

Objectives: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC). Methods: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed. Feature selection was performed using Mann-Whitney U test, LASSO regression, and SelectKBest. Prediction models were established by gaussian process (GP) and decision tree (DT) algorithms and evaluated by the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA). Results: Compared to the DL (AUCtraining = 0.830, AUCtest = 0.779, and AUCvalidation = 0.711), radiomics (AUCtraining = 0.810, AUCtest = 0.710, and AUCvalidation = 0.839), and clinical (AUCtraining = 0.780, AUCtest = 0.685, and AUCvalidation = 0.695) models, the combined model based on the GP algorithm, which consisted of four DL features, five radiomics features, and two clinical variables, not only demonstrated the highest diagnostic efficacy (AUCtraining = 0.949, AUCtest = 0.877, and AUCvalidation = 0.914) but also led to an improvement in risk reclassification of the TP53 mutation (NIRtraining = 66.38%, 56.98%, and 83.48%, NIRtest = 50.72%, 80.43%, and 89.49%, and NIRvalidation = 64.58%, 87.50%, and 120.83%, respectively). In addition, the combined model exhibited good agreement and clinical utility in calibration curves and DCA analyses, respectively. Conclusions: A prediction model based on the GP algorithm and consisting of DL and radiomics features of DWI as well as clinical variables can effectively assess TP53 mutation in EC. [ABSTRACT FROM AUTHOR]

Objectives: To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection. Materials and methods: A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated. Results: Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74–85%) and 78% (95% CI: 70–85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74–84%) and 80% (95% CI: 71–87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83–0.89) and 0.86 (95% CI: 0.83–0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84–0.90] and 0.82 [95% CI: 0.79–0.85], respectively). Conclusion: Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers. Clinical relevance statement: In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments. Key Points: MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions. [ABSTRACT FROM AUTHOR]

This study investigated the use of bi-exponential diffusion-weighted imaging (DWI) combined with structural features to differentiate high-grade glioma (HGG) from solitary brain metastasis (SBM). A total of 57 patients (31 HGG, 26 SBM) who underwent pre-surgical multi-b DWI and structural MRI (T1W, T2W, T1W + C) were included. Volumes of interest (VOI) in the peritumoral edema area (PTEA) and enhanced tumor area (ETA) were selected for analysis. Histogram features of slow diffusion coefficient (Dslow), fast diffusion coefficient (Dfast), and perfusion fraction (frac) were extracted. Results showed that HGG patients had higher skewness of Dfast (P = 0.022) and frac (P = 0.077), higher kurtosis of Dslow (P = 0.019) and frac (P = 0.025), and lower entropy of Dslow (P = 0.005) and frac (P = 0.001) within the ETA. Additionally, HGG exhibited lower mean frac in both ETA (P = 0.007) and PTEA (P = 0.017). Combining skewness of frac in ETA with clear tumor margin enhanced diagnostic performance, achieving an optimal AUC of 0.79. These findings suggest that histogram analysis of diffusion and perfusion characteristics in ETA and structural features can effectively differentiate HGG from SBM. [ABSTRACT FROM AUTHOR]

The Restriction Spectrum Imaging restriction score (RSIrs) has been shown to improve the accuracy for diagnosis of clinically significant prostate cancer (csPCa) compared to standard DWI. Both diffusion and T2 properties of prostate tissue contribute to the signal measured in DWI, and studies have demonstrated that each may be valuable for distinguishing csPCa from benign tissue. The purpose of this retrospective study was to (1) determine whether prostate T2 varies across RSI compartments and in the presence of csPCa, and (2) evaluate whether csPCa detection with RSIrs is improved by acquiring multiple scans at different TEs to measure compartmental T2 (cT2). Data includes two cohorts scanned for csPCa with 3T multi-b-value diffusion-weighted sequences acquired at multiple TEs. cT2 values were computed from multi-TE RSI data and compared by compartment. CsPCa detection was compared between RSIrs and a logistic regression model (LRM) to predict the probability of csPCa using cT2 in combination with RSI measurements. Two-sample t-tests (α = 0.05) and the area under the receiver operating characteristic curve (AUC) were used for the statistical analyses. In both cohorts, T2 was different (p < 0.05) across the four RSI compartments (C1, C2, C3, C4). Voxel-level, cohort 1: T2 was different in csPCa for C1, C2, C3 (p < 0.001). Patient-level, cohort 1: T2 was different in csPCa patients in C3 (p = 0.02); cohort 2: T2 differed in csPCa patients in C1 (p = 0.01), C3 (p = 0.01) and C4 (p < 0.01). Voxel-level csPCa detection: cT2 did not improve discrimination over RSIrs alone (p = 0.9). Patient-level: RSIrs and the LRM performed better than diffusion alone (p < 0.001), but the difference in AUCs between RSIrs and the LRM was not significantly different (p = 0.54). In conclusion, significant differences in cT2 were observed between normal and cancerous prostatic tissue. With our data, however, consideration of cT2 in addition to diffusion did not significantly improve cancer detection performance. [ABSTRACT FROM AUTHOR]

Background: To investigate the efficacy of three-compartment restriction spectrum imaging (RSI), diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in the assessment of lymph node metastases (LNM) in rectal cancer. Methods: A total of 77 patients with rectal cancer who underwent pelvic MRI were enrolled. RSI-derived parameters (f1, f2, and f3), DKI-derived parameters (Dapp and Kapp), and the DWI-derived parameter (ADC) were calculated and compared using a Mann–Whitney U test or independent samples t-test. Logistic regression (LR) analysis was used to identify independent predictors of LNM status. Area under the receiver operating characteristic curve (AUC) and Delong analysis were performed to assess the diagnostic performance of each parameter. Results: The LNM-positive group exhibited significantly higher f1 and Kapp levels and significantly lower f3, Dapp, and ADC levels compared to the LNM-negative group (p < 0.05). There was no difference in f2 levels between the two groups (p = 0.783). LR analysis showed that Dapp and Kapp were independent predictors of a positive LNM status. AUC and Delong analysis showed that DKI (Dapp + Kapp) exhibited significantly higher diagnostic efficacy (AUC = 0.908; sensitivity = 87.10%; specificity = 86.96%) than RSI (f1 + f3) and DWI (ADC), with AUCs were 0.842 and 0.771 (Z = 2.113, 3.453; p = 0.035, < 0.001, respectively). The AUC performance between RSI and DWI was also statistically significant (Z = 1.972, p = 0.049). Conclusion: The RSI model is superior to conventional DWI but inferior to DKI in differentiation between LNM-positive and LNM-negative rectal cancers. Further study is needed before it could serve as a promising biomarker for guiding effective treatment strategies. Critical relevance statement: The three-compartment restriction spectrum imaging was able to differentiate between LNM-positive and LNM-negative rectal cancers with high accuracy, which has the potential to serve as a promising biomarker that could guide treatment strategies. Key Points: Three-compartment restriction spectrum imaging could differentiate lymph node metastases in rectal cancer. Diffusion kurtosis imaging and diffusion-weighted were associated with lymph node metastases in rectal cancer. The combination of different parameters has the potential to serve as a promising biomarker. [ABSTRACT FROM AUTHOR]

Background and Purpose: The role of asymptomatic diffusion‐weighted imaging‐positive (aDWI+) lesions in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients remains unclear, and their radiographic features may differ from those of symptomatic diffusion‐weighted imaging‐positive (sDWI+) lesions. We aimed to investigate the clinicoradiographic characteristics of aDWI+ lesions in CADASIL patients. Methods: We conducted a retrospective analysis using data from the Taiwan CADASIL Registry. aDWI+ lesions were defined as incidentally detected DWI+ lesions without corresponding acute neurological deficits. We compared the baseline clinical characteristics of patients with and without aDWI+ lesions and analyzed their radiological features and evolution in relation to sDWI+ lesions. Results: Among 154 enrolled patients (mean age 62 ± 10 years), 17 (11%) had aDWI+ lesions. Baseline clinical characteristics were similar in the two groups, but those with aDWI+ lesions had more lacunes (median 8 vs. 2), multiple cerebral microbleeds (CMBs; 85% vs. 40%), and anterior temporal white matter hyperintensity (WMH; 47% vs. 14%). Multivariable analysis showed that aDWI+ lesions were associated with anterior temporal WMH (odds ratio 5.7, 95% confidence interval 1.5–21.0) after adjusting for multiple lacunes, multiple CMBs, and total WMH score. Compared to sDWI+ lesions, aDWI+ lesions were more often small infarcts (<1 cm; 89% vs. 23%) and less likely to involve the corticospinal tract (11% vs. 96%). Among the 11 aDWI+ lesions with follow‐up magnetic resonance imaging, seven became microinfarcts, three became lacunes, and one disappeared. Conclusions: aDWI+ lesions in CADASIL are not uncommon and are associated with higher burdens of small vessel disease and anterior temporal WMH. Further research is needed to assess their long‐term impact on CADASIL. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the relationship of diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) parameters with dysfunction in acute focal cerebral ischemia (ACI) rabbits. Methods: The model of ACI in the middle cerebral artery was made using 30 adult male New Zealand rabbits. The dysfunction severities of the ACI rabbits were assessed using Purdy's score. A paired-sample rank sum test was adopted to compare the abnormal signal zone (ASZ) volumes from T2 weighted imaging (T2WI), dynamic susceptibility contrast-enhanced (DSC) imaging, and DWI with a relative cerebral blood flow (rCBF) map; correlations were analyzed between the volume of each ASZ and Purdy's score by Spearman's rank correlation coefficient. The degree of necrotic and apoptotic cells was evaluated in the ASZ from DWI and DSC PWI-DWI mismatch (PDM) zone. Correlations were analyzed between the index of cellular damage and Purdy's score, the volume of ASZs by Spearman's rank correlation coefficient. Results: The ASZ volumes from DSC-PWI and the rCBF maps were larger than those from DWI (p < 0.001 and p < 0.001, respectively); those from the rCBF map (Z = 0.959, p < 0.001) and DSC-PWI (Z = 0.970, p < 0.001) were positively correlated with DWI; a positive correlation was found between Purdy's score and the ASZ volumes from DSC-PWI (Z = 0.889, p < 0.001), DWI (Z = 0.921, p < 0.001), and rCBF (Z = 0.891, p < 0.001). A significant difference was observed between the ASZ from DWI and the PDM zone in terms of the degree of necrotic (p < 0.001) and apoptotic cells (p < 0.001). The degree of cellular damage in the ASZ of DWI and PDM zone had no relationship with Purdy's score and the volumes of ASZs. Conclusion: The ASZ volumes from DSC-PWI, rCBF, and particularly DWI reflected the level of dysfunction in rabbits with ACI. [ABSTRACT FROM AUTHOR]

Neurodevelopmental disorders (NDDs) can severely impact functioning yet effective treatments are limited. Greater insight into the neurobiology underlying NDDs is critical to the development of successful treatments. Using a genetics‐first approach, we investigated the potential of advanced diffusion‐weighted imaging (DWI) techniques to characterize the neural microstructure unique to neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). In this prospective study, children with NF1, NS, and typical developing (TD) were scanned using a multi‐shell DWI sequence optimized for neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI). Region‐of‐interest and tract‐based analysis were conducted on subcortical regions and white matter tracts. Analysis of covariance, principal components, and linear discriminant analysis compared between three groups. 88 participants (Mage = 9.36, SDage = 2.61; 44 male) were included: 31 NS, 25 NF1, and 32 TD. Subcortical regions differed between NF1 and NS, particularly in the thalamus where the neurite density index (NDI; estimated difference 0.044 [95% CI: −0.034, 0.053], d = 2.36), orientation dispersion index (ODI; estimate 0.018 [95% CI: 0.010, 0.026], d = 1.39), and mean kurtosis (MK; estimate 0.049 [95% CI: 0.025, 0.072], d = 1.39) were lower in NF1 compared with NS (all p < 0.0001). Reduced NDI was found in NF1 and NS compared with TD in all 39 white matter tracts investigated (p < 0.0001). Reduced MK was found in a majority of the tracts in NF1 and NS relative to TD, while fewer differences in ODI were observed. The middle cerebellar peduncle showed lower NDI (estimate 0.038 [95% CI: 0.021, 0.056], p < 0.0001) and MK (estimate 0.057 [95% CI: 0.026, 0.089], p < 0.0001) in NF1 compared to NS. Multivariate analyses distinguished between groups using NDI, ODI, and MK measures. Principal components analysis confirmed that the clinical groups differ most from TD in white matter tract‐based NDI and MK, whereas ODI values appear similar across the groups. The subcortical regions showed several differences between NF1 and NS, to the extent that a linear discriminant analysis could classify participants with NF1 with an accuracy rate of 97%. Differences in neural microstructure were detected between NF1 and NS, particularly in subcortical regions and the middle cerebellar peduncle, in line with pre‐clinical evidence. Advanced DWI techniques detected subtle alterations not found in prior work using conventional diffusion tensor imaging. [ABSTRACT FROM AUTHOR]

Background: Insulin resistance (IR) is of growing concern yet its association with white matter integrity remains controversial. We aimed to investigate the association between IR and white matter integrity in nondiabetic adults. Methods: This cross-sectional analysis was conducted based on the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study. A total of 1709 nondiabetic community-dwelling adults with available diffusion-weighted imaging based on brain magnetic resonance imaging and completed oral glucose tolerance test were included. IR was measured noninvasively by insulin sensitivity indices (ISI), including ISIcomposite and ISI0,120, as well as homeostasis model assessment of insulin resistance (HOMA-IR). White matter microstructure abnormalities were identified by diffusion-weighted imaging along with tract-based spatial statistical analysis to compare diffusion metrics between groups. The multivariable linear regression models were applied to measure the association between white matter microstructure abnormalities and IR. Results: A total of 1709 nondiabetic individuals with a mean age of 60.8 ± 6.4 years and 54.1% female were included. We found that IR was associated with a significant increase in mean diffusivity, axial diffusivity, and radial diffusivity extensively in cerebral white matter in regions such as the anterior corona radiata, superior corona radiata, anterior limb of internal capsule, external capsule, and body of corpus callosum. The pattern of associations was more marked for ISIcomposite and ISI0,120. However, the effect of IR on white matter integrity was attenuated after, in addition, adjustment for history of hypertension and cardiovascular disease and antihypertensive medication use. Conclusion: Our findings indicate a significant association between IR and white matter microstructural abnormalities in nondiabetic middle-aged community residents, while these associations were greatly influenced by the history of hypertension and cardiovascular disease, and antihypertensive medication use. Further investigation is needed to clarify the role of IR in white matter integrity, whereas prophylactic strategies of maintaining a low IR status may ameliorate disturbances in white matter integrity. Data accessibility statement: The data that support the findings of this study are available from the corresponding authors upon reasonable request. [ABSTRACT FROM AUTHOR]

To analyze the contribution of two non-standard magnetic resonance imaging (MRI) techniques the chemical-shift image (CSI), and diffusion-weighted imaging (DWI) in distinguishing malignant and benign vertebral bone marrow lesions (VBMLs). Conventional spine MRI protocol, followed by CSI and DWI was performed with a 1.5 T system on 102 oncologic patients between January 2020 and December 2023. From the identified 325 VBMLs, 102 representative lesions (one per patient) were selected. VBMLs were divided into malignant (n = 74) and benign (n = 28) based on histopathology, or imaging follow-up. The quantitative parameters for VBMLs assessment were signal intensity ratio (SIR) derived from CSI and apparent diffusion coefficient (ADC) derived from DWI. The malignant VBMLs had significantly higher SIR values (p < 0.05) and lower ADC values compared to benign VBMLs (p < 0.05). The area under the curve (AUC) was 0.953 (p < 0.001) for SIR, and 0.894 for ADC (p < 0.001) (cut-off at > 0.82, and ≤ 1.57x10−3 mm2/s, respectively). The sensitivity and specificity for SIR were 93.6%, and 88.5%, while for ADC were 88.2% and 92.3% (respectively). The combined use of SIR and ADC improved the diagnostic accuracy to AUC of 0.988 (p < 0.001, cut-off at > 0.19), sensitivity, and specificity of 100.0% and 90.9% (respectively). Quantitative parameters, SIR and ADC, derived from two non-standard MRI techniques, CSI, and DWI, showed diagnostic strength in differentiating malignant and benign VBMLs. Combining both methods can further enhance the diagnostic performance and accuracy of spine MRI in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion encoding gradient waveforms can impart intra‐voxel and inter‐voxel dephasing owing to bulk motion, limiting achievable signal‐to‐noise and complicating multishot acquisitions. In this study, we characterize improvements in phase consistency via gradient moment nulling of diffusion encoding waveforms. Methods: Healthy volunteers received neuro (N=10$$ N=10 $$) and cardiac (N=10$$ N=10 $$) MRI. Three gradient moment nulling levels were evaluated: compensation for position (M0$$ {M}_0 $$), position + velocity (M1$$ {M}_1 $$), and position + velocity + acceleration (M1+M2$$ {M}_1+{M}_2 $$). Three experiments were completed: (Exp‐1) Fixed Trigger Delay Neuro DWI; (Exp‐2) Mixed Trigger Delay Neuro DWI; and (Exp‐3) Fixed Trigger Delay Cardiac DWI. Significant differences (p<0.05$$ p<0.05 $$) of the temporal phase SD between repeated acquisitions and the spatial phase gradient across a given image were assessed. Results: M0$$ {M}_0 $$ moment nulling was a reference for all measures. In Exp‐1, temporal phase SD for Gz$$ {G}_z $$ diffusion encoding was significantly reduced with M1$$ {M}_1 $$ (35% of t‐tests) and M1+M2$$ {M}_1+{M}_2 $$ (68% of t‐tests). The spatial phase gradient was reduced in 23% of t‐tests for M1$$ {M}_1 $$ and 2% of cases for M1+M2$$ {M}_1+{M}_2 $$. In Exp‐2, temporal phase SD significantly decreased with M1+M2$$ {M}_1+{M}_2 $$ gradient moment nulling only for Gz$$ {G}_z $$ (83% of t‐tests), but spatial phase gradient significantly decreased with only M1$$ {M}_1 $$ (50% of t‐tests). In Exp‐3, M1+M2$$ {M}_1+{M}_2 $$ gradient moment nulling significantly reduced temporal phase SD and spatial phase gradients (100% of t‐tests), resulting in less signal attenuation and more accurate ADCs. Conclusion: We characterized gradient moment nulling phase consistency for DWI. Using M1 for neuroimaging and M1 + M2 for cardiac imaging minimized temporal phase SDs and spatial phase gradients. [ABSTRACT FROM AUTHOR]

Background: Carpal tunnel syndrome is the commonest upper limb peripheral neuropathy. Diffusion tensor imaging evaluates the tissue microarchitecture and measures the movement of water protons. It is non-time-consuming, not invasive and not operator dependent. The aim of our study was to evaluate whether diffusion tensor imaging can diagnose carpal tunnel syndrome and whether DTI parameters can correlate with severity of carpal tunnel syndrome. Results: Seventy-two wrists were assessed, 36 diagnosed with carpal tunnel syndrome and 36 age and sex matched controls. FA & ADC were measured at four locations (distal radioulnar joint, proximal, middle and distal carpal tunnel), and the mean for the whole median nerve was calculated. FA & ADC showed statistically significant difference between cases and control at each of the measured four locations and the mean of the whole median nerve. FA & ADC at the hook of hamate (distal CT) showed the most significant difference between cases and control. For FA, the cut-off point at the hook of hamate was 0.5 (sensitivity 94.4%, specificity of 88.9%, positive predictive value 89.5% and negative predictive value 94.1%) and the cut-off point for the mean of the whole nerve was 0.545 (sensitivity 97.22%, specificity 77.78%, positive predictive value 81.4% and negative predictive value 96.6%). For ADC, the cut-off point at the hook of hamate was 1.44 (sensitivity 97.22%, specificity 86.11%, positive predictive value 87.5% and negative predictive value 96.9%) and the cut-off point for the mean of the whole nerve was 1.415 (sensitivity 86.11%, specificity, 83.33%, positive predictive value 83.8% and negative predictive value 85.7%). Conclusion: Diffusion tensor imaging can diagnose carpal tunnel syndrome with accuracy compared to the gold standard nerve conduction studies. Both FA and ADC showed statistically significant differences between cases & controls with FA measurements found to be more significant. [ABSTRACT FROM AUTHOR]

Background: Diffusion-weighted magnetic resonance imaging (DWI) is essential for diagnosing Creutzfeldt–Jakob disease (CJD). Thalamic lesions are rarely detected by DWI in sporadic CJD (sCJD) cases with methionine homozygosity at polymorphic codon 129 (129MM) of the prion protein (PrP) gene. Here, we describe an unusual sCJD case, characterized by prolonged isolated thalamic diffusion hyperintensities and atypical brain pathology, in combination with the 129MM genotype. Case presentation: A 72-year-old Japanese man developed a mild unsteady gait that had persisted for 1 year. DWI revealed isolated thalamic diffusion hyperintensities. Over the following 4 years, his condition progressed to include ataxia and cognitive decline. Repeated cerebrospinal fluid tests were negative for 14-3-3 protein, total tau protein, and real-time quaking-induced conversion assay. Electroencephalography did not show periodic sharp wave complexes or generalized periodic discharges. Despite these findings, thalamic DWI abnormalities persisted and evolved to include cortical lesions in the later stage of the disease. Genetic testing confirmed a 129MM genotype with no pathogenic PrP gene variants. Brain autopsy identified type 2 pathogenic PrP and the absence of the M2-thalamic prion strain, suggesting an MM2-cortical (MM2C)-subtype of sCJD. Histopathology revealed small vacuoles (sv) and patchy-perivacuolar PrP deposits without large vacuoles (lv). Patchy-perivacuolar deposits are a characteristic feature of the MM2C (lv) subtype and indicate MM2C (lv) pathology. Thus, this case was classified as a rare MM2C (sv + lv) subtype. No PrP protein staining was observed in the thalamus, despite spongiform changes with small vacuoles. Conclusions: This case underscores the diagnostic challenges of atypical CJD with isolated thalamic abnormalities on DWI. Despite negative cerebrospinal fluid findings and clinical diagnostic criteria, persistent DWI abnormalities and evolving clinical symptoms continued to raise suspicion of CJD. A definitive diagnosis, being the MM2C (sv + lv) subtype of sCJD, was confirmed upon pathological examination. Even when atypical findings, such as isolated thalamic abnormalities, are observed and various tests are negative, if suspicion of CJD cannot be ruled out, it is important to confirm the diagnosis and pathological subtypes via postmortem analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Design Setting Participants Main Outcome Measure Results Conclusion Apparent diffusion coefficient (ADC) value helps in differentiating infections from neoplasms on magnetic resonance imaging (MRI). We investigate the diffusion‐weighted images in skull base osteomyelitis (SBO) to evaluate if ADC values can differentiate fungal and bacterial SBO and to analyse the microbiology of all SBO patients.Retrospective observational study.Quaternary care referral centre.A retrospective review of 142 patients diagnosed and treated for SBO patients from January 2010 to May 2023 was done.Chi‐square or Fisher's exact test was used to compare ADC values of bacterial and fungal SBO.The most common pathogens isolated were Pseudomonas (42.2%), Aspergillus (30.98%), and S. aureus (23.94%). The average ADC value of affected soft tissues among patients was 1.13 ± 0.26 × 10−3 mm2/s compared to the average ADC value of normal soft tissue, 1.34 ± 0.31 × 10−3 mm2/s. There was no statistical significance when comparing the average ADC values of bacterial and fungal SBO patients (p value = 0.142).This study suggests that though infection due to Pseudomonas was the commonest, it was detected only in 42.2% of patients. More than half of the cases had organisms other than Pseudomonas, demanding the clinician to obtain deeper biopsies early in the course of the disease for microbiological analysis. DWI does not help differentiate bacterial and fungal SBO, again emphasising the need for deeper tissue biopsies in all these patients to assist in the early identification of the pathogen. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic utility of clinical magnetic resonance spectroscopy (MRS) and diffusion-weighted imaging (DWI) in distinguishing between histological grading and isocitrate dehydrogenase (IDH) classification in adult diffuse gliomas. Methods: A retrospective analysis was conducted on 247 patients diagnosed with adult diffuse glioma. Experienced radiologists evaluated DWI and MRS images. The Kruskal-Wallis test examined differences in DWI and MRS-related parameters across histological grades, while the Mann–Whitney U test assessed molecular classification. Receiver Operating Characteristic (ROC) curves evaluated parameter effectiveness. Survival curves, stratified by histological grade and IDH classification, were constructed using the Kaplan–Meier test. Results: The cohort comprised 141 males and 106 females, with ages ranging from 19 to 85 years. The Kruskal-Wallis test revealed significant differences in ADC mean, Cho/NAA, and Cho/Cr concerning glioma histological grade (P <.01). Subsequent application of Dunn's test showed significant differences in ADC mean among each histological grade (P <.01). Notably, Cho/NAA exhibited a marked distinction between grade 2 and grade 3/4 gliomas (P <.01). The Mann–Whitney U test indicated that only ADC mean showed statistical significance for IDH molecular classification (P <.01). ROC curves were constructed to demonstrate the effectiveness of the specified parameters. Survival curves were also delineated to portray survival outcomes categorized by histological grade and IDH classification. Conclusions: Clinical MRS demonstrates efficacy in glioma histological grading but faces challenges in IDH classification. Clinical DWI's ADC mean parameter shows significant distinctions in both histological grade and IDH classification. [ABSTRACT FROM AUTHOR]

Background: In neuroimaging, Magnetic Resonance Imaging (MRI) is a cornerstone technique that allows clinicians to see fine aspects of brain architecture and pathology with unparalleled clarity. Ring-enhancing lesions (RELs) in the brain pose a particularly difficult diagnostic scenario among the many abnormalities found. Objectives: 1. To differentiate neoplastic from nonneoplastic brain lesions using conventional and advanced MR imaging techniques. 2. To study the characteristic imaging findings of various ring enhancing lesions on MRI. Material & Methods: Study Design: A prospective hospital-based observational study. Study area: Department of Radio Diagnosis, Subbaiah Institute of medical sciences, Shivamogga, Karnataka. Study Period: 1 year. Study population: All patients referred to the Department of Radio diagnosis with clinically suspected cerebral ring-enhancing lesions. Sample size: The study consisted of 60 subjects. Sampling method: Simple random technique. Results: Out of 60 patients evaluated, Seizures were the most common presenting complaint in 70% of cases. Headache (18%), fever (6.6%), vomiting (15%), ataxia (5%) and motor weakness (6%) were the other presenting complaints. Our findings were compared with the study conducted by ps mahato8 in which headache (57.5%) was the most common symptom followed by seizures (52.5%). CONCLUSION: From our study, it can be concluded thatMRI is the most sensitive modality in the characterization of intracranial ring-enhancing lesions. MRI being non-invasive and non-radiating is an ideal imaging modality. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to quantify the submandibular gland in suppurative sialadenitis, primary Sjögren's syndrome (pSS) and radiation‐induced sialadenitis using the apparent diffusion coefficient (ADC) for differential diagnosis. Subjects and Methods: This retrospective study included 16 patients with suppurative sialadenitis (n = 9), pSS (n = 3) and radiation‐induced sialadenitis (n = 4) who underwent magnetic resonance imaging between June 2006 and May 2022. The ADC of the submandibular glands in each state was calculated, and the differences were analysed using a one‐way analysis of variance and Tukey's post hoc test. Receiver operating characteristic curves were used to assess the ability of the ADC to distinguish each condition. Statistical significance was set at p < 0.05. Results: The mean ADC value (×10−3 mm2/s) ± standard deviation in the control (non‐affected side of the suppurative sialadenitis group), suppurative sialadenitis, pSS and radiation‐induced groups were 0.94 ± 0.16, 1.24 ± 0.16, 1.33 ± 0.13 and 1.5 ± 0.12, respectively (p < 0.001). The diagnostic value for distinguishing each group was ≥0.75. Conclusion: ADC values are useful for quantitatively assessing and distinguishing submandibular glands in suppurative sialadenitis, primary Sjögren's syndrome and radiation‐induced sialadenitis. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to apply diffusion‐weighted imaging to the evaluation of cervical lymph nodes affected by medication‐related osteonecrosis of the jaw (MRONJ). Methods: This retrospective study analysed the diffusion‐weighted imaging data of 70 patients with or without MRONJ (Stages 0–3). The mean apparent diffusion coefficient (ADC) values of the submandibular (level IB) and superior internal jugular (level IIA) nodes in the control and MRONJ groups were calculated, and differences were analysed using the Mann–Whitney test. Moreover, receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of ADC values to predict lymph nodes that were affected by MRONJ. p < 0.05 was considered statistically significant. Results: The median [interquartile range] (×10−3 mm2/s) of level IB was 0.74 [0.7–0.81] and 0.93 [0.84–1.09] and that of level IIA was 0.79 [0.76–0.85] and 0.97 [0.84–1.06] in the control and MRONJ groups respectively. ROC analysis revealed that the ADC value had excellent ability to discriminate between the control and MRONJ groups. Conclusions: The study findings indicate that diffusion‐weighted imaging can contribute to differentiation of MRONJ from other cervical lymph node diseases and facilitate early detection of MRONJ. [ABSTRACT FROM AUTHOR]

Chronic pain is common in young people and can have a major life impact. Despite the burden of chronic pain, mechanisms underlying chronic pain development and persistence are still poorly understood. Specifically, white matter (WM) connectivity has remained largely unexplored in pediatric chronic pain. Using diffusion-weighted imaging, this study examined WM microstructure in adolescents (age M 5 15.8 years, SD 5 2.8 years) with chronic pain (n 5 44) compared with healthy controls (n 5 24). Neurite orientation dispersion and density imaging modeling was applied, and voxel-based whole-white-matter analyses were used to obtain an overview of potential alterations in youth with chronic pain and tract-specific profile analyses to evaluate microstructural profiles of tracts of interest more closely. Our main findings are that (1) youth with chronic pain showed widespread elevated orientation dispersion compared with controls in several tracts, indicative of less coherence; (2) signs of neurite density tract-profile alterations were observed in several tracts of interest, with mainly higher density levels in patients; and (3) several WM microstructural alterations were associated with pain catastrophizing in the patient group. Implicated tracts include both those connecting cortical and limbic structures (uncinate fasciculus, cingulum, anterior thalamic radiation), which were associated with pain catastrophizing, as well as sensorimotor tracts (corticospinal tract). By identifying alterations in the biologically informative WM microstructural metrics orientation dispersion and neurite density, our findings provide important and novel mechanistic insights for understanding the pathophysiology underlying chronic pain. Taken together, the data support alterations in fiber organization as a meaningful characteristic, contributing process to the chronic pain state. [ABSTRACT FROM AUTHOR]

Objectives: To compare prostate monoparametric MRI (monoMRI), which uses only diffusion-weighted imaging (DWI), with biparametric (bpMRI) and multiparametric MRI (mpMRI) in detecting clinically significant cancer (CSC) and to evaluate the effect of the combination of monoMRI results and prostate-specific antigen (PSA) level. Methods: In this study, 193 patients (average age 70.5 years; average PSA 7.9 ng/mL) underwent prebiopsy MRI and subsequent prostate biopsy from January 2020 to February 2022. Two radiologists independently reviewed the 3 MRI protocols using the Prostate Imaging Reporting and Data System (PI-RADS). Interreader agreement was assessed using the intraclass correlation coefficient (ICC), and diagnostic performance was evaluated with receiver operating characteristic (ROC) curve analysis. The Youden index was used to determine the new cutoff value of PSA for detecting CSCs in patients with negative monoMRI results. Results: CSC was confirmed in 109 patients (56.5%). The interreader agreement on monoMRI (ICC = 0.798) was comparable to that on bpMRI and mpMRI (ICC = 0.751 and 0.714, respectively). ROC curve analysis of the 3 protocols revealed no difference in detecting CSCs (P > 0.05). Applying a new PSA cutoff value (9.5 and 7.4 ng/mL, respectively) in monoMRI-negative patients improved the sensitivity of monoMRI from 89.9% to 96.3% for Reader 1, and from 95.4% to 99.1% for Reader 2. Conclusions: MonoMRI based solely on DWI demonstrated similar diagnostic performance to bpMRI and mpMRI in detecting CSCs, and the combination of PSA level with monoMRI has the potential to effectively triage patients with a high likelihood of CSCs. Advances in knowledge: Monoparametric MRI conducted only with diffusion-weighted imaging (DWI), may show comparable performance to biparametric and multiparametric MRI in detecting clinically significant prostate cancer. In patients with negative monoparametric MRI results, implementing a new PSA cutoff value to determine the need for a biopsy could decrease the number of missed prostate cancer. [ABSTRACT FROM AUTHOR]

Background: Diffusion‐weighted imaging (DWI)‐based virtual MR elastography (DWI‐vMRE) in the assessment of breast lesions is still in the research stage. Purpose: To investigate the usefulness of elasticity values on DWI‐vMRE in the evaluation of breast lesions, and the correlation with the values calculated from shear‐wave elastography (SWE). Study Type: Prospective. Population/Subjects: 153 patients (mean age ± standard deviation: 55 ± 12 years) with 153 pathological confirmed breast lesions (24 benign and 129 malignant lesions). Field Strength/Sequence: 1.5‐T MRI, multi‐b readout segmented echo planar imaging (b‐values of 0, 200, 800, and 1000 sec/mm2). Assessment: For DWI‐vMRE assessment, lesions were manually segmented using apparent diffusion coefficient (ADC0–1000) map, then the region of interests were copied to the map of shifted‐ADC (sADC200–800, sADC 200–1500). For SWE assessment, the shear modulus of the lesions was measured by US elastic modulus (μUSE). Intraclass/interclass kappa coefficients were calculated to measure the consistency. Statistical Tests: Pearson's correlation was used to assess the relationship between sADC and μUSE. A receiver operating characteristic analysis with the area under the curve (AUC) was performed to compare the diagnostic accuracy between benign and malignant breast lesions of sADC and μUSE. A P value <0.05 was considered statistically significant. Results: There were significant differences between benign and malignant breast lesions of μUSE (24.17 ± 10.64 vs. 37.20 ± 12.61), sADC200–800 (1.38 ± 0.31 vs. 0.97 ± 0.18 × 10−3 mm2/sec), and sADC200–1500 (1.14 ± 0.30 vs. 0.78 ± 0.13 × 10−3 mm2/sec). In all breast lesions, a moderate but significant correlation was observed between μUSE and sADC200–800/sADC200–1500 (r = −0.49/−0.44). AUC values to differentiate benign from malignant lesions were as follows: μUSE, 0.78; sADC200–800, 0.89; sADC200–1500, 0.89. Data Conclusions: Both SWE and DWI‐vMRE could be used for the differentiation of benign versus malignant breast lesions. Furthermore, DWI‐vMRE with the use of sADC show relatively higher AUC values than SWE. Level of Evidence: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

PURPOSE To evaluate the diagnostic efficacy of multishot echo-planar imaging (EPI) [RESOLVE (RS)] and non- EPI (HASTE) diffusion-weighted imaging (DWI) in detecting cholesteatoma (CHO), and to explore the role of signal intensity (SI) ratio measurements in addressing diagnostic challenges. METHODS We analyzed RS-EPI and non-EPI DWI images from 154 patients who had undergone microscopic middle ear surgery, with pathological confirmation of their diagnoses. Two radiologists, referred to as Reader A and Reader B, independently reviewed the images without prior knowledge of the outcomes. Their evaluation focused on lesion location, T1-weighted (T1W) signal characteristics, and contrast enhancement in temporal bone magnetic resonance imaging. Key parameters included lesion hyperintensity, size, SI, SI ratio, and susceptibility artifact scores across both imaging modalities. RESULTS Of the patients, 62.3% (96/154) were diagnosed with CHO, whereas 37.7% (58/154) were found to have non-CHO conditions. In RS-EPI DWI, Reader A achieved 89.6% sensitivity, 79.3% specificity, 87.8% positive predictive value (PPV), and 82.1% negative predictive value (NPV). Non-EPI DWI presented similar results with sensitivities of 89.6%, specificities of 86.2%, PPVs of 91.5%, and NPVs of 83.3%. Reader B's results for RS-EPI DWI were 82.3% sensitivity, 84.5% specificity, 89.8% PPV, and 74.2% NPV, whereas, for non-EPI DWI, they were 86.5% sensitivity, 89.7% specificity, 93.3% PPV, and 80% NPV. The interobserver agreement was excellent (RS-EPI, κ: 0.84; non-EPI, κ: 0.91). The SI ratio measurements were consistently higher in non-EPI DWI (Reader A: 2.51, Reader B: 2.46) for the CHO group compared with RS-EPI. The SI ratio cut-off (>1.98) effectively differentiated hyperintense lesions between CHO and non-CHO groups, demonstrating 82.9% sensitivity and 100% specificity, with an area under the curve of 0.901 (95% confidence interval: 0.815-0.956; P < 0.001). Susceptibility artifact scores averaged 1.18 ± 0.7 (Reader A) and 1.04 ± 0.41 (Reader B) in RS-EPI, with non-EPI DWI recording a mean score of 0. CONCLUSION Both RS-EPI and non-EPI DWI exhibited high diagnostic accuracy for CHO. While RS-EPI DWI cannot replace non-EPI DWI, their combined use improves sensitivity. SI ratio measurement in non-EPI DWI was particularly beneficial in complex diagnostic scenarios. CLINICAL SIGNIFICANCE This study refines CHO diagnostic protocols by showcasing the diagnostic capabilities of both RSEPI and non-EPI DWI and highlighting the utility of SI measurements as a diagnostic tool. These findings may reduce false positives and aid in more accurate treatment planning, offering substantial insights for clinicians in managing CHO. [ABSTRACT FROM AUTHOR]

Abstract Objective To assess the utility of multiparametric MRI and clinical indicators in distinguishing nuclear grade and survival of clear cell renal cell carcinoma (ccRCC) complicated with venous tumor thrombus (VTT). Materials and methods This study included 105 and 27 patients in the training and test sets, respectively. Preoperative MRI, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), was performed. Renal lesions were evaluated for IVIM-DWI metrics and conventional MRI features. All the patients had postoperative histologically proven ccRCC and VTT. An expert uropathologist reviewed all specimens to confirm the nuclear grade of the World Health Organization/ International Society of Urological Pathology (WHO/ISUP) of the tumor. Univariate and multivariable logistic regression analyses were used to select the preoperative imaging features and clinical indicators. The predictive ability of the logistic regression model was assessed using receiver operating characteristic (ROC) analysis. Survival curves were plotted using the Kaplan–Meier method. Results High WHO/ISUP nuclear grade was confirmed in 69 of 105 patients (65.7%) in the training set and 19 of 27 patients (70.4%) in the test set, respectively (P = 0.647). Dp_ROI_Low, tumor size, serum albumin, platelet count, and lymphocyte count were independently related to high WHO/ISUP nuclear grade in the training set. The model identified high WHO/ISUP nuclear grade well, with an AUC of 0.817 (95% confidence interval [CI]: 0.735–0.899), a sensitivity of 70.0%, and a specificity of 77.8% in the training set. In the independent test set, the model demonstrated an AUC of 0.766 (95% CI, 0.567–0.966), a sensitivity of 79.0%, and a specificity of 75.0%. Kaplan–Meier analysis showed that the predicted high WHO/ISUP nuclear grade group had poorer progression-free survival than the low WHO/ISUP nuclear grade group in both the training and test sets (P = 0.001 and P = 0.021). Conclusions IVIM-DWI-derived parameters and clinical indicators can be used to differentiate nuclear grades and predict progression-free survival of ccRCC and VTT.

Abstract Objective This study aimed to compare the performance of amide proton transfer–weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant breast lesions, evaluate molecular subtypes of breast cancer, and determine the diagnostic efficacy of the quantitative magnetic resonance imaging (qMRI) parameters in differentiating benign from malignant breast diseases. Methods The study included 168 women who underwent breast APTWI and DKI at Yunnan Cancer Hospital between December 2022 and July 2023. The APT signal intensity (SI), apparent kurtosis coefficient (Kapp), non-Gaussian diffusion coefficient (Dapp), and apparent diffusion coefficient (ADC) values were measured before surgery. The differences in the aforementioned qMRI parameters in molecular subtypes of breast cancer were analyzed using one-way analysis of variance. The efficacy of each quantitative parameter in differentiating benign from malignant breast diseases was evaluated using the receiver-operating characteristic curve. Results Significant differences in qMRI parameters were noted between benign and malignant breast lesions. The Kapp (P

Abstract Background Carpal tunnel syndrome is the commonest upper limb peripheral neuropathy. Diffusion tensor imaging evaluates the tissue microarchitecture and measures the movement of water protons. It is non-time-consuming, not invasive and not operator dependent. The aim of our study was to evaluate whether diffusion tensor imaging can diagnose carpal tunnel syndrome and whether DTI parameters can correlate with severity of carpal tunnel syndrome. Results Seventy-two wrists were assessed, 36 diagnosed with carpal tunnel syndrome and 36 age and sex matched controls. FA & ADC were measured at four locations (distal radioulnar joint, proximal, middle and distal carpal tunnel), and the mean for the whole median nerve was calculated. FA & ADC showed statistically significant difference between cases and control at each of the measured four locations and the mean of the whole median nerve. FA & ADC at the hook of hamate (distal CT) showed the most significant difference between cases and control. For FA, the cut-off point at the hook of hamate was 0.5 (sensitivity 94.4%, specificity of 88.9%, positive predictive value 89.5% and negative predictive value 94.1%) and the cut-off point for the mean of the whole nerve was 0.545 (sensitivity 97.22%, specificity 77.78%, positive predictive value 81.4% and negative predictive value 96.6%). For ADC, the cut-off point at the hook of hamate was 1.44 (sensitivity 97.22%, specificity 86.11%, positive predictive value 87.5% and negative predictive value 96.9%) and the cut-off point for the mean of the whole nerve was 1.415 (sensitivity 86.11%, specificity, 83.33%, positive predictive value 83.8% and negative predictive value 85.7%). Conclusion Diffusion tensor imaging can diagnose carpal tunnel syndrome with accuracy compared to the gold standard nerve conduction studies. Both FA and ADC showed statistically significant differences between cases & controls with FA measurements found to be more significant.

PURPOSE: To evaluate the diagnostic efficacy of multishot echo-planar imaging (EPI) [RESOLVE (RS)] and non-EPI (HASTE) diffusion-weighted imaging (DWI) in detecting cholesteatoma (CHO), and to explore the role of signal intensity (SI) ratio measurements in addressing diagnostic challenges. METHODS: We analyzed RS-EPI and non-EPI DWI images from 154 patients who had undergone microscopic middle ear surgery, with pathological confirmation of their diagnoses. Two radiologists, referred to as Reader A and Reader B, independently reviewed the images without prior knowledge of the outcomes. Their evaluation focused on lesion location, T1-weighted (T1W) signal characteristics, and contrast enhancement in temporal bone magnetic resonance imaging. Key parameters included lesion hyperintensity, size, SI, SI ratio, and susceptibility artifact scores across both imaging modalities. RESULTS: Of the patients, 62.3% (96/154) were diagnosed with CHO, whereas 37.7% (58/154) were found to have non-CHO conditions. In RS-EPI DWI, Reader A achieved 89.6% sensitivity, 79.3% specificity, 87.8% positive predictive value (PPV), and 82.1% negative predictive value (NPV). Non-EPI DWI presented similar results with sensitivities of 89.6%, specificities of 86.2%, PPVs of 91.5%, and NPVs of 83.3%. Reader B’s results for RS-EPI DWI were 82.3% sensitivity, 84.5% specificity, 89.8% PPV, and 74.2% NPV, whereas, for non-EPI DWI, they were 86.5% sensitivity, 89.7% specificity, 93.3% PPV, and 80% NPV. The interobserver agreement was excellent (RS-EPI, κ: 0.84; non-EPI, k: 0.91). The SI ratio measurements were consistently higher in non-EPI DWI (Reader A: 2.51, Reader B: 2.46) for the CHO group compared with RS-EPI. The SI ratio cut-off (>1.98) effectively differentiated hyperintense lesions between CHO and non-CHO groups, demonstrating 82.9% sensitivity and 100% specificity, with an area under the curve of 0.901 (95% confidence interval: 0.815–0.956; P < 0.001). Susceptibility artifact scores averaged 1.18 ± 0.7 (Reader A) and 1.04 ± 0.41 (Reader B) in RS-EPI, with non-EPI DWI recording a mean score of 0. CONCLUSION: Both RS-EPI and non-EPI DWI exhibited high diagnostic accuracy for CHO. While RS-EPI DWI cannot replace non-EPI DWI, their combined use improves sensitivity. SI ratio measurement in non-EPI DWI was particularly beneficial in complex diagnostic scenarios. CLINICAL SIGNIFICANCE: This study refines CHO diagnostic protocols by showcasing the diagnostic capabilities of both RS-EPI and non-EPI DWI and highlighting the utility of SI measurements as a diagnostic tool. These findings may reduce false positives and aid in more accurate treatment planning, offering substantial insights for clinicians in managing CHO.

BackgroundHigh b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa.MethodsOne hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm2 using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm2 via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm2 (sDWI500) and b = 0, b = 500 s/mm2, and b = 1000 s/mm2 (sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC).ResultsWithin the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000 and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively.ConclusionsDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

Abstract Spinal and bulbar muscular atrophy (SBMA), or Kennedy’s disease (KD), is a rare hereditary neuromuscular disorder demonstrating commonalities with amyotrophic lateral sclerosis (ALS). The current study aimed to define functional and central nervous system abnormalities associated with SBMA pathology, their interaction, and to identify novel clinical markers for quantifying disease activity. 27 study participants (12 SBMA; 8 ALS; 7 Control) were recruited. SBMA patients underwent comprehensive motor and sensory functional assessments, and neurophysiological testing. All participants underwent whole-brain structural and diffusion MRI. SBMA patients demonstrated marked peripheral motor and sensory abnormalities across clinical assessments. Increased abnormalities on neurological examination were significantly associated with increased disease duration in SBMA patients (R2 = 0.85, p

Abstract Background and purpose Radiomics offers little explainability. This study aims to develop a radiomics model (Rad-Score) using diffusion-weighted imaging (DWI) to predict high-risk patients for nodal metastasis or recurrence in endometrial cancer (EC) and corroborate with choline metabolism. Materials and methods From August 2015 to July 2018, 356 EC patients were enrolled. Rad-Score was developed using LASSO regression in a training cohort (n = 287) and validated in an independent test cohort (n = 69). MR spectroscopy (MRS) was also used in 230 patients. Nuclear MRS measured choline metabolites in 70 tissue samples. The performance was compared against European Society for Medical Oncology (ESMO) risk groups. A P

Background Breast cancer remains a significant global health concern, with early diagnosis and risk factor identification crucial for improving outcomes. Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) measurements have emerged as promising tools in breast cancer diagnostics. However, the influence of hormonal status on these measurements remains unclear. Objective This scoping review aims to synthesize current evidence on how hormonal changes in pre- and post-menopausal women influence ADC values of benign, malignant, and fibroglandular breast tissues. Method Following the Arksey and O’Malley framework, we conducted a comprehensive search of Scopus, Embase, and PubMed databases for relevant studies published between January 2000 and 2021. Inclusion criteria encompassed 1.5 Tesla MRI studies reporting ADC values in female subjects, considering menopausal status. Results Six studies meeting the inclusion criteria, involving 612 patients, were analyzed. Findings suggest that menopausal status may influence ADC values, with postmenopausal women generally showing lower ADC values in both normal fibroglandular tissue and breast lesions. The impact of menstrual cycle phases on ADC values was less consistent across studies. Conclusions This review highlights the potential influence of hormonal status on ADC values in breast tissues. While DWI with ADC mapping shows promise as a reliable diagnostic tool across different hormonal states, further research is needed to fully understand and account for hormonal influences on ADC measurements. Future studies should focus on longitudinal designs, standardization of DWI protocols, and integration of hormonal status information into breast cancer risk assessment models.

Introduction: The primary objective was to investigate the value of the fluid attenuated inversion recovery (FLAIR) signal intensity ratio (SIR) in identifying stroke within 4.5 h. The secondary objective was to ascertain whether large vessel occlusion (LVO) mediated the relationship between the SIR and stroke within 4.5 h. Methods: We analyzed 633 acute stroke patients within 24 h of clear symptom onset. The SIR and DWI-FLAIR mismatch were evaluated. First, we determined whether demographic variables, vascular risk factors and LVO were related to stroke within 4.5 h with multivariate logistic regression analyses and stratified regression analysis. Next, we used mediation analysis to determine whether LVO explained the association between SIR and stroke within 4.5 h. Finally, we used receiver operating characteristic (ROC) analysis to assess the value of SIR, independent variable, and multiparameter models in identifying stroke within 4.5 h and compared with DWI-FLAIR mismatch. Results: Hyperlipemia, LVO and SIR were associated with stroke within 4.5 h. Mediation analysis revealed that LVO partially mediated the relationship between SIR and stroke within 4.5 h (p < 0.001). The multiparameter model (hyperlipemia, LVO and SIR) showed significantly improved performance (AUC 0.869) in identifying stroke within 4.5 h over DWI-FLAIR mismatch (0.684), hyperlipemia (0.632), LVO (0.667) and SIR (0.773) models. Conclusion: SIR is associated with stroke within 4.5 h, and LVO partially mediates this relationship. A multiparameter model combining hyperlipemia, LVO and SIR can more accurately identify stroke within 4.5 h than individual parameter models. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a leading cause of disability, with its incidence rising in tandem with obesity rates. Traditional imaging methods, such as radiography, are limited in their ability to detect early cartilage changes, necessitating the exploration of advanced imaging techniques. MRI offers a non-invasive method to visualize joint structures, with various sequences providing different insights into cartilage morphology and composition. Methods: We conducted a comparative study involving 100 OA patients, utilizing multiple MRI sequences to assess joint cartilage. Each patient underwent imaging with the following sequences: T2 mapping, T2* mapping, T1 rho, dGEMRIC, gagCEST, sodium imaging, and DWI. Image quality, cartilage visualization, and sensitivity to cartilage degeneration were evaluated for each sequence. Quantitative measurements were taken to assess cartilage thickness, composition, and structural integrity.Results: •T2 Mapping: Effective in assessing cartilage hydration and collagen network integrity. Provided clear images of cartilage structure but was less sensitive to early biochemical changes. •T2 Mapping: * Similar to T2 mapping but offered improved sensitivity to iron and other paramagnetic substances within the cartilage. •T1 Rho: Excellent for detecting early biochemical changes in cartilage, particularly proteoglycan content. •dGEMRIC: Provided detailed information on glycosaminoglycan (GAG) concentration, a key marker of cartilage health. •gagCEST: Offered high specificity for GAG concentration, though image acquisition times were longer. •Sodium Imaging: Directly measured sodium content, correlating with GAG concentration. However, required specialized equipment and longer scan times. •DWI: Sensitive to changes in the microstructure of cartilage, offering insights into early degeneration processes. Conclusion: Advanced compositional MRI techniques, particularly T1 rho and dGEMRIC, hold significant promise for the early detection and monitoring of OA. While traditional morphological sequences like T2 mapping remain valuable for structural assessment, integrating these advanced techniques can enhance the diagnostic accuracy and treatment planning for OA patients. Further research is needed to streamline these techniques for widespread clinical adoption. [ABSTRACT FROM AUTHOR]

Some patients with intracerebral hemorrhage show lesions on diffusion-weighted magnetic resonance imaging, and such lesions have been associated with a greater risk of worse prognosis. Here we meta-analyzed the available evidence for such an association. Studies that reported the presence or absence of lesions on diffusion-weighted imaging (DWI) after intracerebral hemorrhage as well as clinical or radiological outcomes were systematically reviewed and meta-analyzed. Clinical outcome was defined as a score of modified Rankin scale (mRS) at admission to 90 days. Ten studies involving 3575 patients were included in the meta-analysis, and the incidence of DWI lesions ranged from 11.1% to 49.6%. Lesions were associated with a significantly higher risk of poor outcome (mRS scores 3–6) across 6 studies (odds ratio: 2.91; 95% confidence interval: 1.62–5.23; P < 0.001). In subgroup analysis, mRS scores 4–6 were associated with the presence of lesions on DWI (odds ratio: 2.18; 95% confidence interval: 1.31–3.60; P = 0.003). We observed similar results using 3 different definitions of lesions on DWI. Some studies have reported that recurrence of intracerebral hemorrhage was also related with DWI lesions. But there was controversy on the relationship between mortality, ischemic stroke, and hematoma volume and DWI lesions. Lesions on DWI after intracerebral hemorrhage were associated with a higher risk of poor outcome, but large longitudinal studies are needed to verify this association. [ABSTRACT FROM AUTHOR]

Objectives: Basilar artery occlusion (BAO) may be etiologically attributed to embolism or in situ thrombosis due to basilar stenosis (BS). Patients with BAO due to BS (BAOS) are known to have worse outcomes than patients with embolic occlusions (BAOE). BAOS occurs more proximally in the basilar artery (BA) than BAOE. We hypothesize that differing brain stem infarct patterns contribute to outcome differences between these stroke etiologies. Methods: This retrospective study includes 199 consecutive patients with BAO who received endovascular treatment at a single center. Final infarction in brain parenchyma dependent on the posterior circulation was graded semiquantitatively on magnetic resonance imaging (MRI). Associations to underlying stenosis and angiographic and clinical outcome variables were tested. The primary endpoint was early good clinical outcome (EGCO, mRS score ≤ 3 at discharge). Results: Infarct extension of the medulla oblongata (OR = 0.25; 95% CI = 0.07–0.86; p = 0.03), the inferior pons (OR = 0.328; 95% CI = 0.17–0.63; p = 0.001), the superior pons (OR = 0.57; 95% CI = 0.33–0.99; p = 0.046), and the occipital lobes (OR = 0.46; 95% CI = 0.26–0.80; p = 0.006) negatively predicted EGCO. Infarct extension for other posterior-circulation-dependent brain regions was not independently associated with unfavorable early outcomes. Patients with BAOS had more proximal occlusions and greater infarct volumes in the inferior brain stem. Successful reperfusion (mTICI 2b-3) occurred more often in patients with BAOE than in BAOS (BAOE: 131 (96.3%); BAOS: 47 (83.9%), p = 0.005). Conclusion: Unfavorable early outcomes in patients with BAOS may be explained by a higher likelihood of inferior brain stem infarcts and lower rates of reperfusion success. Clinical relevance statement: Basilar artery occlusion due to underlying stenosis is associated with a poorer prognosis than that caused by embolism; these results suggest that aggressive endovascular therapy, usually involving the placement of a permanent stent, may be warranted in these patients. Key Points: Inferior brain stem and occipital infarcts are prognostically unfavorable in basilar artery occlusion. Basilar artery occlusion due to stenosis occurs more proximally and is associated with worse outcomes. Differentiating etiologies of basilar artery occlusion may influence how aggressively treated the occlusion is. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. Methods: ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. Results: A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). Conclusions: The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. Clinical relevance statement: Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. Key Points: • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h. [ABSTRACT FROM AUTHOR]

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]

Purpose: We aimed to investigate the diagnostic feasibility of an adjusted diffusion-weighted imaging (DWI) lexicon using multiple b values to assess breast lesions according to DWI-based breast imaging reporting and data system (BI-RADS). Methods: This Institutional Review Board (IRB)-approved prospective study included 127 patients with suspected breast cancer. Breast MRI was performed using a 3T scanner. Breast DW images were acquired using five b-values of 0, 200, 800, 1000, and 1500 s/mm2 (5b-value DWI) on 3T MRI. Two readers independently assessed lesion characteristics and normal breast tissue using DWI alone (5b-value DWI and 2b-value DWI with b = 0 and 800 s/mm2) according to DWI-based BI-RADS and in combination with the standard dynamic contrast-enhanced images (combined MRI). Interobserver and intermethod agreements were assessed using kappa statistics. The specificity and sensitivity of lesion classification were evaluated. Results: Ninety-five breast lesions (39 malignant and 56 benign) were evaluated. Interobserver agreement for lesion assessment on 5b-value DWI was very good (k = 0.82) for DWI-based BI-RADS categories, lesion type, and mass characteristics; good (k = 0.75) in breast composition; and moderate (k = 0.44) in background parenchymal signal (BPS) and non-mass distribution. Intermethod agreement between assessments performed using either 5b-value DWI or combined MRI was good-to-moderate (k = 0.52-0.67) for lesion type; moderate (k = 0.49-0.59) for DWI-based BI-RADS category and mass characteristics; and fair (k = 0.25-0.40) for mass shape, BPS, and breast composition. The sensitivity and positive predictive values (PPVs) for 5b-value DWI were 79.5%, 84.6% and 60.8%, 61.1% for each reader, respectively; 74.4%, 74.4% and 63.0%, 61.7% for 2b-value DWI; and 97.4%, 97.4% and 73.1%, 76.0% for combined MRI. The specificity and negative predictive values (NPVs) were 64.3%, 62.5% and 81.8%, 85.4% for 5b-value DWI; 69.6%, 67.9% and 79.6%, 79.2% for 2b-value DWI; and 75.0%, 78.6% and 97.7%, 97.8% for combined MRI. Conclusion: Good observer agreement was observed in the 5b-value DWI. The 5b-value DWI based on multiple b-values might have the potential to complement the 2b-value DWI; however, their diagnostic performance tended to be inferior to that of combined MRI for the characterization of breast tumors. [ABSTRACT FROM AUTHOR]