Li HW, Zhu ZY, Sun YF, Yuan CY, Wang MH, Wang N, Xue YW. Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors. World J Gastroenterol 2024; 30(40): 4354-4366 [PMID: 39494097 DOI: 10.3748/wjg.v30.i40.4354]
Corresponding Author of This Article
Ying-Wei Xue, PhD, Professor, Surgical Oncologist, Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin 150081, Heilongjiang Province, China. xueyingwei@hrbmu.edu.cn
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Hong-Wei Li, Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
Zi-Yu Zhu, Ying-Wei Xue, Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
Yu-Fei Sun, Department of Anesthesia, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
Chao-Yu Yuan, Mo-Han Wang, Nan Wang, Department of Computer Science and Technology, Heilongjiang University, Harbin 150000, Heilongjiang Province, China
Co-first authors: Hong-Wei Li and Zi-Yu Zhu.
Co-corresponding authors: Nan Wang and Ying-Wei Xue.
Author contributions: Li HW and Zhu ZY participated in the design of the study and the writing of the manuscript, and they made equal contribution to the manuscript; Sun YF and Yuan CF was involved in data collection and statistical analysis; Wang MH performed the visualization of the research results; Wang N and Xue YW participated in the revision of the manuscript and approved the final manuscript. They made equal contribution to the manuscript. All authors contributed to the article and approved the submitted version.
Supported bythe Nn10 Program of Harbin Medical University Cancer Hospital, China, No. Nn10 PY 2017-03.
Institutional review board statement: This study was approved by the Ethics Committee of Harbin Medical University Cancer Hospital (Ethics Approval No. 2019-185) and all participants provided written informed consent. The study design and implementation strictly adhered to the Declaration of Helsinki and relevant ethical guidelines.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data involved in this study can be obtained from the corresponding author.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ying-Wei Xue, PhD, Professor, Surgical Oncologist, Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin 150081, Heilongjiang Province, China. xueyingwei@hrbmu.edu.cn
Received: July 8, 2024 Revised: September 19, 2024 Accepted: September 27, 2024 Published online: October 28, 2024 Processing time: 99 Days and 17.1 Hours
Abstract
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.
Core Tip: This study identified predictive markers and developed machine learning models to assess the prognosis of patients with gastric cancer and treated with immune checkpoint inhibitors. Key findings highlighted the significance of peripheral blood markers such as platelet count/(lymphocyte count × serum prealbumin), prognostic nutrition index, and body mass index in predicting overall survival and progression-free survival. eXtreme Gradient Boosting was the most effective model for prediction and outperformed traditional methods. These insights underscore the potential of machine-learning algorithms in personalized medicine and emphasize the role of nutritional status in treatment outcomes of patients with gastric cancer.
