A radiomics-based artificial intelligence model to assess the risk of relapse in localized colon cancer

Accurately estimating relapse risk in localized colon cancer (LCC) remains a challenge, as clinicopathological staging often fails to differentiate patients with a higher likelihood of recurrence. There is a need for novel tools to improve patient selection for post-operative chemotherapy. Radiomics has emerged as a powerful, noninvasive approach that may enhance clinical decision making.

This retrospective study selected consecutive stage II and III LCC patients operated with curative intent from 2015 to 2017 in two academic institutions. Patients were assigned to either a training cohort made up of 80% of them or a test cohort, to further validate the initial findings. Penalized Cox proportional hazards and gradient boosted algorithms were designed to estimate time to relapse following a five-fold cross-validation process. Three models were assessed: (i) based only on clinical and pathological features, (ii) on radiomic features alone, and (iii) including clinical/pathological and radiomic variables. A new 'Risk Classification' score was generated based on the best risk assessment.

A total of 278 patients were included in both cohorts. The Cox model trained with clinical and imaging variables showed the highest prognostic power, with a C-index of 0.68 and a mean cumulative dynamic area under the curve (AUC) of 0.69 on the test set. Feature screening identified 20 variables, including clinical data, radiomics features, and fractal features. SHapley Additive exPlanations (SHAP) analysis highlighted factors related to geometry, vascular invasion, and tumor stage as significant variables related to relapse. The new 'Risk Classification' score was able to identify patients with high risk of relapse both in univariable [hazard ratio (HR) 14.22, 95% confidence interval (CI) 1.91-106.08, P = 0.010] and multivariable (HR 11.74, 95% CI, 1.54-89.34, P = 0.017) models.

Risk analysis revealed the new 'Risk Classification' variable as the one with the highest prognostic power compared with the ones currently used. Our findings suggest the potential for improved time-to-relapse estimation, enabling better patient stratification.