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.

SUMMARY BackgroundThe association between meat consumption and the risk of hepatocellular carcinoma (HCC) is still inconclusive.

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Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

The source of IgA and the mechanism for deposition of IgA in the mesangium remain unknown for primary IgA nephropathy. Because CD19 ϩ CD5ϩ B cells are important producers of IgA and contribute to several autoimmune diseases, they may play an important role in IgA nephropathy. In this study, flow cytometry, quantitative PCR, and confocal microscopy were used to assess the frequency, distribution, Ig production, CD phenotypes, cytokine production, and sensitivity to apoptosis of CD19 In the three patients who had IgA nephropathy and did not respond to treatment, the frequency of CD19 ϩ CD5 ϩ B cells did not change. CD19 ϩ CD5 ϩ B cells isolated from patients with untreated IgA nephropathy expressed higher levels of IgA, produced more IFN-␥, and were more resistant to CD95L-induced apoptosis than cells isolated from control subjects and patients with lupus; these properties reversed with effective treatment of IgA nephropathy. In conclusion, these results strongly suggest that CD19 ϩ CD5 ϩ B cells play a prominent role in the pathogenesis of primary IgA nephropathy.