PT  - JOURNAL ARTICLE
AU  - JUNICHI MAZAKI
AU  - KENJI KATSUMATA
AU  - YUKI OHNO
AU  - RYUTARO UDO
AU  - TOMOYA TAGO
AU  - KENTA KASAHARA
AU  - HIROSHI KUWABARA
AU  - MASANOBU ENOMOTO
AU  - TETSUO ISHIZAKI
AU  - YUICHI NAGAKAWA
AU  - AKIHIKO TSUCHIDA
TI  - A Novel Prediction Model for Colon Cancer Recurrence Using Auto-artificial Intelligence
AID  - 10.21873/anticanres.15276
DP  - 2021 Sep 01
TA  - Anticancer Research
PG  - 4629--4636
VI  - 41
IP  - 9
4099  - http://ar.iiarjournals.org/content/41/9/4629.short
4100  - http://ar.iiarjournals.org/content/41/9/4629.full
SO  - Anticancer Res2021 Sep 01; 41
AB  - 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.