Purpose To evaluate the role of magnetization transfer (MT) magnetic resonance (MR) imaging for the characterization of intestinal fibrosis compared with contrast material-enhanced and diffusion-weighted MR imaging and its capability for differentiating fibrotic from inflammatory strictures in humans with Crohn disease (CD) by using surgical histopathologic analysis as the reference standard. Materials and Methods Institutional review board approval and informed consent were obtained for this prospective study. Abdominal MT imaging, contrast-enhanced imaging, and diffusion-weighted imaging of 31 consecutive patients with CD were analyzed before elective surgery. The bowel wall MT ratio normalized to skeletal muscle, the apparent diffusion coefficient (ADC), and the percentage of enhancement gain were calculated; region-by-region correlations with the surgical specimen were performed to determine the histologic degree of fibrosis and inflammation. The performance of MT imaging was validated in five new patients. One-way analysis of variance test, Spearman rank correlation, and receiver operating characteristic curve were used for statistical analysis. Results Normalized MT ratios strongly correlated with fibrosis scores (r = 0.769; P = .000) but did not correlate with inflammation scores (r = -0.034; P = .740). Significant differences (F = 49.002; P = .000) in normalized MT ratios were found among nonfibrotic, mildly, moderately, and severely fibrotic walls. The normalized MT ratios of mixed fibrotic and inflammatory bowel walls were significantly higher than those of bowel walls with only inflammation present (t = -8.52; P = .000). A high accuracy of normalized MT ratios was shown with an area under the receiver operating characteristic curve (AUC) of 0.919 (P = .000) for differentiating moderately to severely fibrotic bowel walls from nonfibrotic and mildly fibrotic bowel walls, followed by ADC (AUC, 0.747; P = .001) and the percentage of enhancement gain (AUC, 0.592; P = .209). The sensitivity, specificity, and AUC of MT imaging for diagnosing moderate to severe fibrosis in the validation data set were 80% (12 of 15), 100% (three of three), and 0.9 (P = .033), respectively. Conclusion MT imaging outperforms ADC and contrast-enhanced imaging in detecting and distinguishing varying degrees of bowel fibrosis with or without coexisting inflammation. MT imaging could potentially be used as a method to differentiate fibrotic from inflammatory intestinal strictures in patients with CD. RSNA, 2018 Online supplemental material is available for this article.

Objective To investigate the imaging features observed in preoperative Gd-EOB-DTPA-dynamic enhanced MRI and correlated with the presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods 66 HCCs in 60 patients with preoperative Gd-EOB-DTPA-dynamic enhanced MRI were retrospectively analyzed. Features including tumor size, signal homogeneity, tumor capsule, tumor margin, peritumor enhancement during mid-arterial phase, peritumor hypointensity during hepatobiliary phase, signal intensity ratio on DWI and apparent diffusion coefficients (ADC), T1 relaxation times, and the reduction rate between pre- and postcontrast enhancement images were assessed. Correlation between these features and histopathological presence of MVI was analyzed to establish a prediction model. Results Histopathology confirmed that MVI were observed in 17 of 66 HCCs. Univariate analysis showed tumor size (p = 0.003), margin (p = 0.013), peritumor enhancement (p = 0.001), and hypointensity during hepatobiliary phase (p = 0.004) were associated with MVI. A multiple logistic regression model was established, which showed tumor size, margin, and peritumor enhancement were combined predictors for the presence of MVI (α = 0.1). R2 of this prediction model was 0.353, and the sensitivity and specificity were 52.9% and 93.0%, respectively. Conclusion Large tumor size, irregular tumor margin, and peritumor enhancement in preoperative Gd-EOB-DTPA-dynamic enhanced MRI can predict the presence of MVI in HCC.

CTE adds unique information to colonoscopy in differential diagnosis between Crohn's disease and ITB, allowing correct diagnosis in most patients.

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Introduction: The pathological grading of pancreatic neuroendocrine neoplasms (pNENs) is an independent predictor of survival and indicator for treatment. Deep learning (DL) with a convolutional neural network (CNN) may improve the preoperative prediction of pNEN grading. Methods: Ninety-three pNEN patients with preoperative contrast-enhanced computed tomography (CECT) from Hospital I were retrospectively enrolled. A CNN-based DL algorithm was applied to the CECT images to obtain 3 models (arterial, venous, and arterial/venous models), the performances of which were evaluated via an eightfold cross-validation technique. The CECT images of the optimal phase were used for comparing the DL and traditional machine learning (TML) models in predicting the pathological grading of pNENs. The performance of radiologists by using qualitative and quantitative computed tomography findings was also evaluated. The best DL model from the eightfold cross-validation was evaluated on an independent testing set of 19 patients from Hospital II who were scanned on a different scanner. The Kaplan-Meier (KM) analysis was employed for survival analysis. Results: The area under the curve (AUC; 0.81) of arterial phase in validation set was significantly higher than those of venous (AUC 0.57, p = 0.03) and arterial/venous phase (AUC 0.70, p = 0.03) in predicting the pathological grading of pNENs. Compared with the TML models, the DL model gave a higher (although insignificantly) AUC. The highest OR was achieved for the p ratio <0.9, the AUC and accuracy for diagnosing G3 pNENs were 0.80 and 79.1% respectively. The DL algorithm achieved an AUC of 0.82 and an accuracy of 88.1% for the independent testing set. The KM analysis showed a statistical significant difference between the predicted G1/2 and G3 groups in the progression-free survival (p = 0.001) and overall survival (p < 0.001). Conclusion: The CNN-based DL method showed a relatively robust performance in predicting pathological grading of pNENs from CECT images.