Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study.

ABSTRACT

BACKGROUND: Research on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival. AIM: To investigate the prognosis of GMA and develop predictive model. METHODS: From the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA's prognosis. RESULTS: This model had an AUC of 0.7433 [95% confidence intervals (95%CI) 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors' use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA Regional nodes examined [hazard ratio (HR) 0.98, 95%CI 0.97-0.98, P < 0.001)] and chemotherapy (HR 0.62, 95%CI 0.58-0.66, P < 0.001). CONCLUSION: The deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.