BackgroundThe preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision‐making.PurposeTo investigate the performance of T2‐weighted (T2W) MRI‐based deep learning (DL) and radiomics methods for PM evaluation in EOC patients.Study TypeRetrospective.PopulationFour hundred seventy‐nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]).Field Strength/Sequence1.5 or 3 T/fat‐suppression T2W fast or turbo spin‐echo sequence.AssessmentResNet‐50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision‐level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated.Statistical TestsReceiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two‐tailed P < 0.05 was considered significant.ResultsThe ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789).Data ConclusionsT2W MRI‐based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision‐making.Evidence Level4Technical EfficacyStage 2

Background Accurate preoperative diagnosis of post-hepatectomy liver failure (PHLF) is particularly important to improve the prognosis of patients. Purpose To evaluate the predictive value of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) for post-hepatectomy liver failure. Material and Methods A systematic search was performed in the PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles published up to December 2021. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity. Results In total, 13 articles were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the area under the summary receiver operating characteristic curves were 88% (95% confidence interval [CI] = 0.80–0.94), 80% (95% CI = 0.73–0.86), 4.4 (95% CI = 3.3–5.9), 0.14 (95% CI = 0.08–0.25), 31 (95% CI = 17–57), and 0.91 (95% CI = 0.89–0.94), respectively. There was no publication bias and threshold effect in our study. Conclusion Gd-EOB-DTPA-enhanced MRI is a potentially useful for the prediction of PHLF after major hepatectomy.