Can machine learning predict vessels that encapsulates tumor cluster ptterns and histological differentiation in solitary small (< 5 cm) hepatocellular carcinoma?

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. Machine learning (ML) may enable the noninvasive prediction of histopathological features using clinical and imaging data. This study evaluated the utility of ML in predicting vessels that encapsulate tumor clusters (VETC) and tumor differentiation in HCC. We retrospectively analyzed 232 patients who underwent hepatic resection for solitary HCC (≤ 5 cm). Histological assessment was used to determine the VETC status and tumor differentiation. Contrast-enhanced CT images were processed using BiomedCLIP to extract 512-dimensional image feature vectors, which were combined with clinical data and analyzed using a support vector machine classifier. The model performance was evaluated using five-fold cross-validation with precision, recall, and F1-score. VETC-positive tumors were significantly associated with a poor survival. Models using clinical data alone demonstrated limited predictive performance (F1 = 0.469 for VETC; F1 = 0.473 for differentiation). The incorporation of image features modestly improved VETC prediction (F1 = 0.599) but did not enhance the prediction of tumor differentiation. Image-based models did not outperform the clinical models. VETC is a histopathological marker associated with a poor prognosis in HCC. While ML models showed limited predictive accuracy, further optimization of imaging analysis and data integration may improve noninvasive histological prediction.