Anticancer research · 2026
Prediction of the Clinicopathological Prognosis of Colon Cancer After Radical Resection Using Artificial Intelligence "TabNet".
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Kosuge Tatsufumi, Mazaki Junichi, Kasahara Kenta, Osakabe Hiroaki, Kuwabara Hiroshi, Iwasaki Kenichi, Oguma Junya, Koga Hiroyuki, Kanazawa Akishige, Nagakawa Yuichi
Аннотация
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.
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.