Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors  

作　　者：Hong-Wei Li Zi-Yu Zhu Yu-Fei Sun Chao-Yu Yuan Mo-Han Wang Nan Wang Ying-Wei Xue 

机构地区：[1]Department of Gastrointestinal Surgery,Harbin Medical University Cancer Hospital,Harbin 150081,Heilongjiang Province,China [2]Department of Gastroenterological Surgery,Harbin Medical University Cancer Hospital,Harbin 150081,Heilongjiang Province,China [3]Department of Anesthesia,Harbin Medical University Cancer Hospital,Harbin 150081,Heilongjiang Province,China [4]Department of Computer Science and Technology,Heilongjiang University,Harbin 150000,Heilongjiang Province,China

出　　处：《World Journal of Gastroenterology》2024年第40期4354-4366,共13页世界胃肠病学杂志（英文）

基　　金：Supported by the Nn10 Program of Harbin Medical University Cancer Hospital,China,No.Nn10 PY 2017-03.

摘　　要：BACKGROUND Although immune checkpoint inhibitors(ICIs)have demonstrated significant survival benefits in some patients diagnosed with gastric cancer(GC),existing prognostic markers are not universally applicable to all patients with advanced GC.AIM To investigate biomarkers that predict prognosis in GC patients treated with ICIs and develop accurate predictive models.METHODS Data from 273 patients diagnosed with GC and distant metastasis,who un-derwent≥1 cycle(s)of ICIs therapy were included in this study.Patients were randomly divided into training and test sets at a ratio of 7:3.Training set data were used to develop the machine learning models,and the test set was used to validate their predictive ability.Shapley additive explanations were used to provide insights into the best model.RESULTS Among the 273 patients with GC treated with ICIs in this study,112 died within 1 year,and 129 progressed within the same timeframe.Five features related to overall survival and 4 related to progression-free survival were identified and used to construct eXtreme Gradient Boosting(XGBoost),logistic regression,and decision tree.After comprehensive evaluation,XGBoost demonstrated good accuracy in predicting overall survival and progression-free survival.CONCLUSION The XGBoost model aided in identifying patients with GC who were more likely to benefit from ICIs therapy.Patient nutritional status may,to some extent,reflect prognosis.

关 键 词：Gastric cancer Machine learning Immune checkpoint inhibitors Web-based calculator Progression-free survival Overall survival 

分 类 号：R730.3[医药卫生—肿瘤]