Construction of a Post-operative Multimodal Survival Prediction Model for Patients With Colorectal Liver Metastases: A Study Based on the Fusion of a Multicenter Cohort and High-dimensional Radiomics

This study is based on a strictly screened multicenter cohort of patients with colorectal liver metastases who underwent surgical intervention. First, high-throughput quantitative features are automatically extracted from the 3D regions of interest (ROIs) segmented based on pre-operative magnetic resonance imaging (MRI). Advanced machine learning dimensionality reduction algorithms are subsequently applied to eliminate redundant variables and select core imaging signatures that deeply reflect tumor micro-heterogeneity, microvascular proliferation, and invasive status. On this basis, these micro-level radiomics features are fused with macro-level clinical parameters, including liver function indexes and tumor load markers. Multivariable survival analysis models are performed to identify independent prognostic factors, which are further used to develop a visual and intuitive predictive nomogram tool. Finally, time-dependent evaluation metrics and an external validation cohort will be utilized to systematically test the dynamic predictive performance and cross-platform generalizability of the model. This multimodal dual-track data paradigm aims to achieve a non-invasive and efficient "digital optical biopsy" approach for prognostic evaluation.