Prognostic artificial intelligence model to predict 5 year survival at 1 year after gastric cancer surgery based on nutrition and body morphometry. Issue 2 (12th February 2023)

• Record Type: Journal Article
• Authors: Chung, Heewon; Ko, Yousun; Lee, In‐Seob; Hur, Hoon; Huh, Jimi; Han, Sang‐Uk; Kim, Kyung Won; Lee, Jinseok
• Is Part Of: Journal of cachexia, sarcopenia and muscle. Volume 14:Issue 2(2023)
• Page Start: 847
• Page End: 859
• Publication Date: 2023-02-12
• Subjects: Gastric cancer -- Survival -- Prediction -- Prognosis -- Artificial intelligence; Cachexia -- Periodicals; Muscles -- Aging -- Periodicals; Muscles -- Periodicals; Cachexia; Sarcopenia; Muscles; Cachexia; Muscles; Muscles -- Aging; Periodicals; Periodicals; 616
• DOI: 10.1002/jcsm.13176
• Languages: English
• ISSNs: 2190-5991

Abstract

Abstract: Background: Personalized survival prediction is important in gastric cancer patients after gastrectomy based on large datasets with many variables including time‐varying factors in nutrition and body morphometry. One year after gastrectomy might be the optimal timing to predict long‐term survival because most patients experience significant nutritional change, muscle loss, and postoperative changes in the first year after gastrectomy. We aimed to develop a personalized prognostic artificial intelligence (AI) model to predict 5 year survival at 1 year after gastrectomy. Methods: From a prospectively built gastric surgery registry from a tertiary hospital, 4025 gastric cancer patients (mean age 56.1 ± 10.9, 36.2% females) treated gastrectomy and survived more than a year were selected. Eighty‐nine variables including clinical and derived time‐varying variables were used as input variables. We proposed a multi‐tree extreme gradient boosting (XGBoost) algorithm, an ensemble AI algorithm based on 100 datasets derived from repeated five‐fold cross‐validation. Internal validation was performed in split datasets ( n = 1121) by comparing our proposed model and six other AI algorithms. External validation was performed in 590 patients from other hospitals (mean age 55.9 ± 11.2, 37.3% females). We performed a sensitivity analysis to analyse the effect of the nutritional and fat/muscle indices using a leave‐one‐out method. Results: In the internal validation, our proposed model showed AUROC of 0.8237, which outperformed the other AI algorithms (0.7988–0.8165), 80.00% sensitivity, 72.34% specificity, and 76.17% balanced accuracy. In the external validation, our model showed AUROC of 0.8903, 86.96% sensitivity, 74.60% specificity, and 80.78% balanced accuracy. Sensitivity analysis demonstrated that the nutritional and fat/muscle indices influenced the balanced accuracy by 0.31% and 6.29% in the internal and external validation set, respectively. Our developed AI model was published on a website for personalized survival prediction. Conclusions: Our proposed AI model provides substantially good performance in predicting 5 year survival at 1 year after gastric cancer surgery. The nutritional and fat/muscle indices contributed to increase the prediction performance of our AI model.