Virtual liver biopsy for detecting steatohepatitis in chemotherapy-treated patients: comparison with whole-liver radiomics and external validation

OBJECTIVES: Chemotherapy-associated steatohepatitis (CASH) increases the risk of postoperative liver failure and mortality in patients undergoing liver surgery. However, non-invasive diagnosis remains an unmet need. Radiomics has potential for characterizing liver parenchyma. This study evaluated the performance of a virtual liver biopsy for CASH prediction, comparing it with whole-liver radiomic analysis. MATERIALS AND METHODS: Two retrospective series of patients undergoing hepatectomy for colorectal liver metastases were analyzed: the public "Colorectal-Liver-Metastases" dataset (training and internal validation) and a monocenter series from Humanitas Research Hospital (external validation). Inclusion criteria were preoperative chemotherapy (≥ 2 months) and post-chemotherapy CT (thickness ≤ 5 mm). IBSI-compliant radiomic features were extracted from whole-liver parenchyma and from a virtual biopsy (manually segmented 2-mL cylinder of non-tumoral liver parenchyma). Estimated liver volume (eLV) was computed using three orthogonal diameters. Four XGBoost models were developed (70/30 split, 5-fold cross-validation): clinical; clinical + VB-radiomics (ViB); clinical + VB-radiomics + eLV (ViBE+); clinical + whole-liver radiomics (WholeLiver). RESULTS: One hundred eighty patients were included: 105 internal (63 males, mean age 58.9 years, 38% CASH+) and 75 external (44 males, mean age 62.4 years, 19% CASH+). WholeLiver and ViBE+ models achieved excellent and comparable performance in internal validation (WholeLiver: AUC = 0.857 (95% BS-CI: 0.676-0.954), accuracy = 0.774 (95% BS-CI: 0.613-0.903); ViBE + : AUC = 0.831 (95% BS-CI: 0.549-0.956), accuracy = 0.839 (95% BS-CI: 0.710-0.935); p = 0.814), outperforming ViB model (AUC = 0.724 (95% BS-CI: 0.483-0.896), accuracy = 0.774 (95% BS-CI: 0.613-0.903)). SHAP analysis confirmed liver volume metrics as key predictors. In external validation, the ViBE+ model achieved AUC = 0.745 (95% BS-CI: 0.604-0.886) and accuracy = 0.680 (95% BS-CI: 0.562-0.783). CONCLUSION: Virtual biopsy provides a reproducible, non-invasive radiomic sampling strategy for CASH prediction. Combined with liver volume metrics, it achieves performance comparable to whole-liver analysis, supporting its potential clinical application. KEY POINTS: Question Despite its association with postoperative morbidity, the non-invasive preoperative assessment of CASH remains challenging in patients undergoing liver surgery. Findings Radiomic analysis based on a virtual liver biopsy, combined with volumetric measurements, showed excellent performance in predicting CASH, comparable to whole-liver analysis, with liver volume emerging as a key predictor. Critical Relevance Statement A standardized virtual biopsy-based radiomic analysis represents a promising non-invasive approach for evaluating liver parenchymal injury, as demonstrated in preoperative assessment of CASH and identification of high-risk surgical patients.