Colorectal cancer sidedness: prognostic implications and the predictive role of artificial intelligence

Colorectal cancer (CRC) is a biologically heterogeneous disease in which tumor sidedness has emerged as a relevant prognostic factor. Conventional TNM staging does not incorporate several clinically and biologically meaningful variables that may influence outcomes. In this context, artificial intelligence (AI) based approaches offer an opportunity to integrate complex clinicopathological data and improve prognostic stratification. This study aimed to evaluate clinicopathological variables associated with tumor sidedness and to identify clinical predictors of high-risk disease using an AI-based decision-tree model. This retrospective cohort study included 71 adults who underwent surgical resection for colorectal adenocarcinoma at a tertiary oncology center between 2020 and 2024 and had complete clinicopathological data available for analysis. Overall and progression-free survival were estimated using the Kaplan–Meier method, and associations between categorical variables were assessed using Fisher’s exact test. Decision-tree models were constructed using the J48 (C4.5) algorithm, and model performance was evaluated by leave-one-out cross-validation (LOOCV). Left-sided tumors were predominant and more frequently associated with alcohol ingestion (p = 0.04), the use of neoadjuvant chemoradiotherapy (p < 0.01), and higher mortality (p = 0.04), despite more intensive treatment strategies. Right-sided tumors were prevalent in women and were associated with angiolymphatic invasion. In prognostic modeling, positive surgical margins emerged as the strongest predictor of mortality (Full 85.18%; LOOCV 74.07%). Among patients with negative margins, tumor laterality represented the most influential prognostic factor, with right-sided tumors associated with improved survival. Interestingly, younger patients showed shorter progression-free survival (Full 89.09%; LOOCV 76.36%). Tumor sidedness constitutes a meaningful prognostic dimension in CRC when integrated with established pathological factors. AI-based decision-tree models can capture clinically coherent prognostic signatures and complement traditional staging systems, supporting their role as hypothesis-generating tools for individualized risk assessment and guiding future prospective validation.