Development of a machine learning model for predicting right-sided colon cancer recurrence: A retrospective single center pilot study

Abstract

Introduction and aim

Right-sided colon cancer (RSCC) is characterized by distinct clinical features and recurrence patterns. Our study aimed to develop a predictive model for distant recurrence in patients with RSCC who underwent curative surgery, employing the Random Forest machine learning algorithm, based on clinical and histopathologic variables.

Materials and methods

A retrospective analysis of 64 patients treated within the time frame of 2016–2024 was conducted. The variables included age, sex, lymphovascular invasion, and number of lymph nodes evaluated (transformed for inverse interpretation). Oversampling was employed to balance the dataset and a Random Forest model for predicting distant recurrence (defined as that occurring at least six months after surgery) was constructed. Its performance was evaluated through accuracy, sensitivity, F1 score, and area under the ROC curve (AUC).

Results

The model achieved an AUC of 0.76 in the test set, with 75% sensitivity and 100% specificity. The most relevant variables were low lymph node harvest, older age, male sex, and lymphovascular invasion. A simplified model with those four variables maintained 95% accuracy. A clinical risk scale based on cumulative scores was developed that classified patients into low-risk and high-risk groups, with distant recurrence rates of 8.3% and 56.3%, respectively.

Conclusion

The predictive model showed a robust capacity for stratifying the distant recurrence risk, supporting the use of machine learning algorithms as a complementary tool in the individualized management of RSCC.