Integrated CT Foundation Model and Radiomic Features Define Clinically and Biologically Distinct Colorectal Cancer...

Colorectal cancer is clinically heterogeneous, and current risk stratification does not fully capture differences in recurrence, survival, or treatment response. Molecular subtyping has improved biological classification, but sequencing-based workflows are not always available before treatment or at the population scale. We developed a survival-informed computed tomography (CT) phenotyping framework that combines CT foundation-model representations with PyRadiomics descriptors of tumor shape, intensity, and texture. Across 4 nonoverlapping cohorts, including SYSU6H cohort 1 (N = 902), SYSU6H cohort 2 (N = 3,267), external Ningbo cohort 1 (N = 509), and SYSU6HNAT neoadjuvant-treatment cohort (N = 709), fused CT features were selected and integrated to derive survival-informed CT imaging phenotypes, followed by evaluation of survival, adjusted prognostic associations, molecular correlates, phenotype assignment, and treatment response. The fused representation identified 2 survival-informed imaging phenotypes with marked survival differences. Compared with CT-S1, CT-S2 was associated with worse disease-free survival and overall survival (disease-free survival hazard ratio = 11.16, 95% confidence interval 9.19 to 13.55; overall survival hazard ratio = 14.29, 95% confidence interval 10.83 to 18.85; both P < 0.0001) and remained associated with outcomes after adjustment for clinicopathologic variables and measured tumor size. CT-S2 was enriched for consensus molecular subtype 4-like composition and stromal/extracellular matrix programs, suggesting transcriptomic and microenvironment-associated correlates linked to the adverse imaging phenotype. A flexible phenotype-assignment model reproduced prognostic separation in internal and external cohorts, and assigned phenotypes were associated with survival and pathological response in SYSU6HNAT. These findings suggest that integrated CT phenotyping may provide a noninvasive approach for identifying survival-informed colorectal cancer imaging phenotypes with exploratory molecular correlates, supporting further independent validation.