Nomogram for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma using DCE-CT based intratumoral heterogeneity quantification and body fat distribution: A multicenter study.

Background

Vessels encapsulating tumor clusters (VETC) represent a distinct vascular architecture associated with metastasis in hepatocellular carcinoma (HCC). This study aimed to develop and externally validate a nomogram integrating intratumor heterogeneity (ITH), radiologic, clinical, and body composition features for preoperative VETC prediction.

Methods

This two-center retrospective study included 360 patients with pathologically confirmed VETC status between January 2017 and June 2024. Tumors were clustered using k-means with the elbow method to derive an ITH Score quantifying spatial heterogeneity. Based on dynamic contrast-enhanced computed tomography (DCE-CT), five machine learning classifiers constructed four DCE-CT-based models: clinical-radiological-body composition (CRB), ITH Score, Rad Score, and an integrated nomogram. Performance was evaluated using area under the curve (AUC), calibration, and decision curve analysis (DCA). A two-round reader study assessed nomogram assistance. Recurrence-free survival (RFS) was analyzed using Kaplan-Meier methods.

Results

Independent predictors included intratumor necrosis, non-smooth tumor margin, total adipose tissue index (TATI), and portal venous phase ITH Score. Using a logistic regression (LR) algorithm, the integrated nomogram showed the highest discrimination in the internal and external test sets (AUCs, 0.847 and 0.814, respectively), with numerically higher AUCs than the other models. Calibration and DCA demonstrated good agreement and clinical utility. Nomogram assistance improved accuracy, particularly for the junior radiologist. VETC- patients exhibited significantly longer RFS than VETC + patients (P < 0.0001), while higher nomogram scores correlated with shorter RFS (log-rank P < 0.05).

Conclusion

The integrated nomogram enables preoperative VETC prediction, improves radiologists' performance, and facilitates prognostic stratification in HCC.