Development and validation of prognostic models for colon cancer incorporating extramural vascular invasion assessed by contrast-enhanced computed tomography

Abstract

BACKGROUND

Extramural vascular invasion (EMVI) represents a crucial high-risk pathological feature in colon cancer, strongly linked to lymph node involvement, distant dissemination, and local recurrence. Advanced analytical techniques such as machine-learning and nomogram modeling enable the integration of multifactorial prognostic variables, facilitating precise risk stratification and individualized therapeutic decision-making. We postulated that computed tomography (CT)-detected EMVI (ctEMVI) served as an independent determinant of disease-free survival (DFS) in patients with colon cancer, and incorporating ctEMVI into machine-learning-driven predictive models alongside clinicopathological variables, within a nomogram-based framework, would enable more accurate individualized postoperative risk stratification and improve DFS prediction.

AIM

To develop and validate interpretable DFS prediction models for colon cancer by combining multiple machine-learning algorithms with a nomogram framework based on ctEMVI.

METHODS

In this retrospective analysis, comprehensive clinical, radiological, and pathological information was collected from 101 patients who underwent curative resection for colon cancer. Based on findings from preoperative contrast-enhanced CT, patients were stratified into ctEMVI-positive and ctEMVI-negative groups. DFS was estimated using Kaplan-Meier methods and compared with log-rank tests. Prognostic variables independently associated with DFS were identified through Cox proportional hazards regression and subsequently integrated into machine-learning-based predictive models and a nomogram framework. The discriminative ability, calibration accuracy, clinical utility, and interpretability of these models were systematically evaluated using receiver operating characteristic curves, calibration plots, decision curve analysis, and SHapley Additive exPlanations.

RESULTS

Among 101 patients, 40 were ctEMVI-positive and 61 ctEMVI-negative. The ctEMVI was significantly associated with T staging determined by CT, lymph node status determined by CT, pathological T stage, pathological nodal status, lymph node ratio, lymphovascular invasion, and perineural invasion (all P < 0.05). During follow-up, 29 patients experienced recurrence or metastasis, including 19 in the ctEMVI-positive group, which showed significantly poorer DFS. Multivariate Cox analysis identified ctEMVI, pathological nodal status, lymphovascular invasion, and perineural invasion as independent predictors of DFS. The nomogram demonstrated good performance, with area under the receiver operating characteristic curve values of 0.791 (95%CI: 0.644-0.937) for 1-year and 0.796 (95%CI: 0.686-0.906) for 3-year DFS, while CoxBoost achieved the best 2-year DFS prediction (area under the receiver operating characteristic curve = 0.773, 95%CI: 0.659-0.886). SHapley Additive exPlanations analysis confirmed model interpretability and variable importance.

CONCLUSION

The ctEMVI was a significant prognostic factor in colon cancer, and CoxBoost and nomogram models accurately predicted DFS after curative resection.

Keywords: Colon cancer; Computed tomography-detected extramural vascular invasion; Disease-free survival; Machine learning; Nomogram

Core Tip: Extramural vascular invasion represents a well-established determinant of prognosis in colon cancer; however, its reliable evaluation before surgery remains clinically challenging. The present study provided evidence that computed tomography-detected extramural vascular invasion (ctEMVI) functioned as an independent predictor of disease-free survival following curative resection. By incorporating ctEMVI with conventional clinicopathological factors, we developed interpretable machine-learning-based prediction models and a nomogram to enhance individualized preoperative risk assessment. Among the evaluated algorithms, CoxBoost demonstrated the strongest predictive capability. Collectively, these results underscored the clinical utility of ctEMVI-driven modeling approaches for refining preoperative risk stratification in patients with colon cancer.