Tumor-to-liver volume ratio (TLVR)-integrated Radiomics-clinicopathological Fusion Model for Prognosis Prediction in Colorectal Cancer Liver Metastases

Background Colorectal cancer (CRC) ranks as the third most common malignancy worldwide, with colorectal liver metastasis (CRLM) being the leading cause of CRC-related mortality. In this study, we propose the tumor-to-liver volume ratio (TLVR) as a standardized physiological biomarker and develop a multimodal fusion model integrating TLVR, CT radiomics and clinicopathological factors to accurately predict overall survival (OS) in CRLM patients. Methods In this retrospective study, 218 CRLM patients were enrolled and randomly divided into a training cohort (n = 152) and a validation cohort (n = 66). Radiomic features and clinical data were extracted from treatment-naive CT scans and medical records. The cut-off value for TLVR was determined by receiver operating characteristic (ROC) curve analyses. The random survival forest algorithm was used to construct the clinical, radiomics, and fusion models. The model performance was assessed with C-index, time-dependent area under the curve (AUC), and decision curve analysis (DCA). Results TLVR ≥ 0.015 (1.5%) was significantly correlated with poorer OS for CRLM patients. Fifteen radiomic features and five clinical variables were incorporated for model construction. The fusion model demonstrated superior prognosic accuracy in the validation cohort with AUC of 0.855, compared to the clinical model (AUC = 0.831) and the radiomics model (AUC = 0.828). Conclusion TLVR is a potent prognostic biomarker reflecting tumor-host volumetric equilibrium. Its integration into a CT radiomics-clinical fusion model significantly enhances OS prediction accuracy for CRLM patients. This non-invasive tool enables personalized therapeutic strategies, including TLVR-guided adjuvant therapy allocation and avoidance of futile conversion surgery.