Identification of histologic grading of urothelial carcinoma still depends on histopathologic examination. As an emerging and promising imaging technology, radiomic texture analysis is a noninvasive technique and has been studied to differentiate various tumors. This study explored the value of computed tomography (CT) texture analysis for the differentiation of low-grade urothelial carcinoma (LGUC), high-grade urothelial carcinoma (HGUC), and their invasive properties. Radiologic data were analyzed retrospectively for 94 patients with pathologically proven urothelial carcinomas from November 2016 to April 2019. Pathologic examination demonstrated that tumors were: high grade in 43 cases, and low grade in 51 cases; and nonmuscle invasive (NMI) in 37 cases, and muscle invasive (MI) in 37 cases. Maximum tumor diameters on CT scan were manually outlined as regions of interest and 78 texture features were extracted automatically. Three-phasic CT images were used to measure texture parameters, which were compared with postoperative pathologic grading and invasive results. The independent sample t test or Mann–Whitney U test was used to compare differences in parameters. Receiver-operating characteristic curves for statistically significant parameters were used to confirm efficacy. Of the 78 features extracted from each phase of CT images, 26 (33%), 20 (26%), and 22 (28%) texture parameters were significant (P < .05) for differentiating LGUC from HGUC, while 19 (24%), 16 (21%), and 30 (38%) were significant (P < .05) for differentiating NMI from MI urothelial carcinoma. Highest areas the under curve for differentiating grading and invasive properties were obtained by variance (0.761, P < .001) and correlation (0.798, P < .001) on venous-phase CT images. Texture analysis has the potential to distinguish LGUC and HGUC, or NMI from MI urothelial carcinoma, before surgery.

To evaluate whether the magnetic resonance imaging (MRI) proton density fat fraction (PDFF) can predict the biological characteristics of hepatocellular carcinoma (HCC) preoperatively. A total of 131 HCCs were included. The MRI features and PDFF values were evaluated by two independent radiologists. The intraclass correlation coefficient (ICC) was calculated in terms of inter- and intra-observer agreements. The macrotrabecular-massive (MTM) subtype, microvascular invasion (MVI) status, histological grade, and proliferative status of Ki-67 and p53 were identified in HCCs. The diagnostic performance of the PDFF was evaluated using receiver operating characteristic (ROC) curve analysis based on the area under the receiver operating characteristic curve (AUC). PDFF values showed significant differences between: MTM vs. non-MTM HCCs (p=0.048), MVI-positive vs. negative tumors (p=0.041), high- vs. low-grade lesions (p<0.001), and p53-positive vs. negative cases (p=0.015), but not for Ki-67 expression (p=0.075). The AUC values of the PDFF for predicting the MTM subtype, MVI status, histological grade, and proliferative status of p53 were 0.606, 0.588, 0.683, and 0.671, respectively. Only infiltrative appearance had significant difference between MVI-positive and MVI-negative groups. Combining PDFF with infiltrative appearance significantly improved MVI prediction (AUC = 0.681, p = 0.02). MRI-PDFF demonstrates potential as a quantitative biomarker for preoperative assessment of HCC aggressiveness, particularly for the MTM subtype, histological grade and p53 status, though its standalone performance for MVI prediction remains limited. Integration with morphological features enhances diagnostic accuracy, supporting its complementary role in multiparametric HCC characterization. Not applicable.

Objectives: To develop a diagnostic model for predicting the early recurrence of microvascular invasion (MVI)-negative hepatocellular carcinoma (HCC) after surgical resection, using the Liver Imaging Reporting and Data System (LI-RADS) version 2018. Methods: This retrospective study included 73 patients with MVI-negative HCC who underwent Gadoxetic acid-enhanced MRI (EOB-MRI) scanning before surgical resection. The clinical factors and LI-RADS v2018 MRI features associated with early recurrence were determined using univariable and multivariable analyses. A diagnostic model predicting early recurrence after surgical resection was developed, and its predictive ability was evaluated via a receiver operating characteristic curve. Then, the recurrence-free survival (RFS) rates were analyzed by Kaplan–Meier method. Results: In total, 26 (35.6%) patients were diagnosed with early recurrence according to the follow-up results. Infiltrative appearance and targetoid hepatobiliary phase (HBP) appearance were independent predictors associated with early recurrence (p < 0.05). For the established diagnostic model that incorporated these two significant predictors, the AUC value was 0.76 (95% CI: 0.64–0.85) for predicting early recurrence after resection, which was higher than the infiltrative appearance (AUC: 0.67, 95% CI: 0.55–0.78, p = 0.019) and targetoid HBP appearance (AUC: 0.68, 95% CI:0.57–0.79, p = 0.028). In the RFS analysis, patients with infiltrative appearance and targetoid HBP appearance showed significantly lower RFS rates than those without infiltrative appearance (2-year RFS rate, 48.0% vs. 72.0%; p = 0.009) and targetoid HBP appearance (2-year RFS rate, 60.0% vs. 35.0%; p = 0.003). Conclusion: An EOB-MRI model based on infiltrative appearance and targetoid HBP appearance showed good performance in predicting early recurrence of HCC after surgery, which may provide personalized guidance for clinical treatment decisions in patients with MVI-negative HCC.