한빛사논문
- 조회575
Heewon Chung1, Yousun Ko2, In-Seob Lee3, Hoon Hur4, Jimi Huh5, Sang-Uk Han4, Kyung Won Kim2* & Jinseok Lee1*
1Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea;
2Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea;
3Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea;
4Department of Surgery, Ajou University School of Medicine, Suwon, Republic of Korea;
5Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
*Correspondence to: Kyung Won Kim ; Jinseok Lee
Heewon Chung, Yousun Ko, and In-Seob Lee contributed equally to this work as first authors
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.
논문정보
- Research article
- 2023년 02월 (BRIC 등록일 2023-02-20)
- 국내 연구진
- 바이오·의료융합 > 의료기기
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