ArticleAbdominal radiology (New York)2026
Three-class radiomic differentiation of hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma on multiphasic contrast-enhanced CT.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposePreoperative 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.
methodsA 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.
resultsOf 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.
conclusionThese 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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