BackgroundPreoperative estimation of hepatocellular carcinoma (HCC) recurrence after conventional transcatheter arterial chemoembolization (c‐TACE) is crucial for subsequent follow‐up and therapy decisions.PurposeTo evaluate the associations of radiomics models based on pretreatment contrast‐enhanced MRI, a clinical‐radiological model and a combined model with the recurrence‐free survival (RFS) of patients with HCC after c‐TACE, and to develop a radiomics nomogram for individual RFS estimations and risk stratification.Study TypeRetrospective.PopulationIn all, 184 consecutive HCC patients.Field Strength/Sequence1.5T or 3.0T, including T2WI, T1WI, and contrast‐enhanced T1WI.AssessmentAll HCC patients were randomly divided into the training (n = 110) and validation datasets (n = 74). Radiomics signatures capturing intratumoral and peritumoral expansion (1, 3, and 5 mm) were constructed, and the radiomics models were set up using least absolute shrinkage and selection operator (LASSO) Cox regression. Clinical‐radiological features were identified by univariate and multivariate Cox regression. The clinical‐radiological model and the combined model fusing the radiomics signature with the clinical‐radiological risk factors were developed by a multivariate Cox proportional hazard model. A radiomics nomogram derived from the combined model was established.Statistical TestsLASSO Cox regression, univariate and multivariate Cox regression, Kaplan–Meier analysis were performed. The discrimination performance of each model was quantified by the C‐index.ResultsAmong the different peritumoral expansion models, only the 3‐mm peritumoral expansion model (C‐index, 0.714) showed a comparable performance (P = 0.4087) to that of the portal venous phase intratumoral model (C‐index, 0.727). The combined model showed the best performance and the C‐index was 0.802. Kaplan–Meier analysis showed that the cutoff values of the combined model relative to a median value (1.7426) perfectly stratified these patients into high‐risk and low‐risk subgroups.Data ConclusionThe combined model is more valuable than the clinical‐radiological model or radiomics model alone for evaluating the RFS of HCC patients after c‐TACE, and the radiomics nomogram can be used to preoperatively and individually estimate RFS.Level of Evidence: 3Technical Efficacy Stage: 4J. Magn. Reson. Imaging 2020;52:461–473.

Background: To compare the diagnostic performance of radiomics models with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters for the preoperative prediction of extramural venous invasion (EMVI) in rectal cancer patients and to develop a preoperative nomogram for predicting the EMVI status.Methods: In total, 106 rectal cancer patients were enrolled in our study. All patients under went preoperative rectal high-resolution MRI and DCE-MRI. We built five models based on the perfusion parameters of DCE-MRI (quantitative model), the radiomics of T 2 -weighted (T 2 W) CUBE imaging (R 1 model), DCE-MRI (R 2 model), clinical features (clinical model), and clinical-radiomics features. The predictive efficacy of the radiomics signature was assessed and internally verified. The area under the receiver operating curve (AUC) was used to compare the diagnostic performance of different radiomics models and DCE-MRI quantitative parameters. The radiomics score and clinical-pathologic risk factors were incorporated into an easy-to-use nomogram.Results: The quantitative parameters K trans and Ve were significantly higher in the EMVI-positive group than in the EMVI-negative group (both P =0.02). K trans combined with Ve showed a fair degree of accuracy (AUC 0.680 in the training cohort and AUC 0.715 in the validation cohort) compared with K trans or Ve alone. The AUCs of the R 1 and R 2 models were 0.826, 0.715 and 0.872, 0.812 in the training and validation cohorts, respectively. In addition, the R 2 -C model yielded an AUC of 0.904 in the training cohort and 0.812 in the validation cohort. The nomogram was presented based on the clinical-radiomics model. The calibration curves showed good agreement.Yu et al. Radiomics to Predict EMVI Conclusion:The radiomics nomogram that incorporates the radiomics score, histopathological grade and T stage demonstrated better diagnostic accuracy than the DCE-MRI quantitative parameters and may have significant clinical implications for the preoperative individualized prediction of EMVI in rectal cancer patients.

Background: The noninvasive assessment of hepatic inflammatory activity (HIA) is crucial for making clinical decisions and monitoring therapeutic efficacy in chronic liver disease (CLD). Purpose: To develop MRI-based radiomics models by extracting features from the whole liver and localized regions of the right liver lobe, compare the efficiency of two radiomics models, and further develop a radiomics nomogram for the assessment of HIA in CLD. Study Type: Retrospective. Population: In all, 137 consecutive patients. Field Strength/Sequence: 1.5T/T 2-weighted imaging. Assessment: All patients (nonsignificant HIA, n = 98; significant HIA, n = 39) were randomly divided into a training (n = 95) and a test cohort (n = 42). Radiomics features were extracted from the regions covering the whole liver (ROI-w) and localized regions of the right liver lobe (ROI-r). Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses were used to select features and develop radiomics models. A combined model fusing the valuable radiomics features with clinical-radiological predictors was developed. Finally, a radiomics nomogram derived from the combined model was developed. Statistical Tests: Synthetic minority oversampling technique algorithm, LASSO, receiver operator characteristic curve, and calibration curve analysis were performed. Results: The area under the curve (AUC), sensitivity, and specificity of the ROI-w radiomics model in assessing HIA were 0.858, 0.800, and 0.733, respectively. The ROI-r model were 0.844, 0.733, and 0.867, respectively. No differences were detected between the two radiomics models (P = 0.8329). The combined model fusing valuable ROI-w radiomics features, albumin, and periportal edema exhibited a promising performance (AUC, 0.911). The calibration curves showed good agreement between the actual observations and nomogram predictions. Data Conclusion: The MRI-based radiomics models had a powerful ability to evaluate HIA and the ROI-w radiomics model was comparable to the ROI-r model. Moreover, the radiomics nomogram could be a favorable method to individually estimate HIA in CLD.