Development and independent validation of explainable radiomics-based machine learning models for prognosis in colorectal liver metastases

Explainable ML models leveraging both lesion-centric and contextual liver radiomics offer clinically transparent prediction of recurrence and survival in CRLM. SHAP highlighted clinically plausible, reproducible imaging determinants, enabling risk stratification. The validation of specific radiomic determinants suggests the potential practical utility of this approach, laying out the groundwork for integrating with DL and multi-omic data in future oncology strategies.