Abstract
BACKGROUND: Vessels encapsulating tumor clusters (VETC) serve as a crucial adverse prognostic indicator in hepatocellular carcinoma (HCC). This study aimed to develop and validate a Delta radiomics-based nomogram model on dynamic contrast-enhanced CT (DCE-CT) to predict VETC status and patient prognosis in HCC. METHODS: A cohort of 222 patients from two centers with HCC undergoing DCE-CT scans and CD34 immunochemical staining was enrolled. Each liver lesion was segmented on intratumoral and peritumoral regions in the arterial phase (AP) and portal vein phase (PP) CT images. A total of 10,128 (1,688*6) radiomics features, including absolute and relative delta radiomics features, were extracted. Using four machine-learning algorithms, the features were trained and optimized (training set), and validated (internal and external test sets) to classify VETC patterns. Multivariable logistic regression incorporating signature scores and clinical predictors generated the nomogram. Model performance was evaluated through area under the curves (AUC) analysis, calibration curves, and decision curve analysis (DCA). The Kaplan-Meier survival analysis was used to assess recurrence-free survival (RFS) in the VETC+ and VETC- patients. RESULTS: The logistic regression-based nomogram incorporating three radiomic signatures and two clinical factors showed powerful predictive ability in internal and external test sets with AUCs of 0.854 and 0.803, respectively. The calibration curves, DCA showed favorable predictive performance of the nomogram. Patients classified as high-risk by the nomogram exhibited significantly shorter RFS compared to low-risk counterparts (P < 0.001). CONCLUSION: The developed nomogram demonstrated clinical translatability in preoperative VETC prediction and recurrence risk stratification, providing a potential imaging biomarker for guiding personalized therapeutic strategies in HCC management.