A Novel Prediction Model for Colon Cancer Recurrence Using Auto-artificial Intelligence

Abstract

Background/aim: We aimed to develop a novel recurrence prediction model for stage II-III colon cancer using simple auto-artificial intelligence (AI) with improved accuracy compared to conventional statistical models.

Patients and methods: A total of 787 patients who had undergone curative surgery for stage II-III colon cancer between 2000 and 2018 were included. Binomial logistic regression analysis was used to calculate the effect of variables on recurrence. The auto-AI software 'Prediction One' (Sony Network Communications Inc.) was used to predict recurrence with the same dataset used for the conventional statical model. Predictive accuracy was assessed by the area under the receiver operating characteristic curve (AUC).

Results: The AUC of the multivariate model was 0.719 (95%CI=0.655-0.784), whereas that of the AI model was 0.815, showing a significant improvement.

Conclusion: This auto-AI prediction model demonstrates improved accuracy compared to the conventional model. It could be constructed by clinical surgeons who are not familiar with AI.

Keywords: Artificial intelligence; auto-AI; colon cancer; prediction model; recurrence.