Three-class radiomic differentiation of hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma on multiphasic contrast-enhanced CT.

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Abstract

Preoperative differentiation of hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-CCA) remains clinically challenging due to overlapping imaging features. This study evaluated whether radiomic features from multiphasic contrast-enhanced CT can simultaneously differentiate these three primary liver cancer subtypes using a bias-aware machine learning framework. A publicly available four-phase contrast-enhanced CT (CECT) dataset of 278 pathologically confirmed patients (94 HCC, 99 ICC, 85 cHCC-CCA) was analyzed. Intratumoral, peritumoral, boundary, and background-liver radiomic features (n = 373) were compared across classes using the Kruskal-Wallis test with false discovery rate correction. An XGBoost classifier was trained within a nested cross-validation framework comparing four feature-selection strategies; the SHAP-based top-K strategy was retained as the primary model. Model significance was confirmed by permutation testing, and misclassified cases were characterized using key discriminative features. Of 373 tested features, 316 (84.7%) showed significant intergroup differences after correction, led by arterial-phase liver-parenchyma reference attenuation ([Formula: see text]= 0.438). The compact SHAP-based model achieved a nested cross-validated AUC of 0.953 ± 0.030 (out-of-fold macro AUC 0.948; permutation p < 0.001). In a separate ablation analysis, background-liver features alone approached full-model performance, exceeding intratumoral features alone. Of 278 patients, 48 (17.3%) were misclassified, predominantly at the ICC-cHCC-CCA boundary; errors reflected attenuation values shifted toward the confounding class, except in cHCC-CCA, where no examined variable distinguished correctly from misclassified cases. These findings are hypothesis-generating and require external validation; radiomic features describing the tumor-liver interface and peritumoral microenvironment showed high cross-validated discriminative performance and, pending prospective validation, may inform non-invasive assessment in cases of diagnostic uncertainty.

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