Prediction of the Clinicopathological Prognosis of Colon Cancer After Radical Resection Using Artificial Intelligence "TabNet".

BACKGROUND/AIM: The recurrence rate of colon cancer after radical resection remains high at approximately 14%. Furthermore, evidence regarding the indications for adjuvant chemotherapy (Adj) for stage II/III colon cancer is limited. In this study, we used artificial intelligence (AI) to create a clinicopathological recurrence prediction model after radical resection for colon cancer, and we identified factors that predict disease recurrence.

PATIENTS AND

METHODS

This study included 326 patients who underwent radical resection for stage II/III colon cancer at the Tokyo Medical University Hospital between 2000 and 2015. Analysis was performed using the AI system TabNet, with clinicopathological factors as covariates.

RESULTS

The recurrence prediction accuracy was 96% in the Traininig group and 92% in the Test group. TabNet successfully identified the following recurrence prediction factors, ranked in descending order of the contribution importance: TNM-stage, Adj status, N-stage, venous invasion (v), histology, T-stage, sex, lymphatic invasion (Ly), and age.

CONCLUSION

Analysis using AI enabled the creation of a recurrence prediction model with higher accuracy than previously reported models. The application of Adj for colorectal cancer is anticipated to be refined in the future, leading to lower recurrence rates.