ArticleWorld journal of gastroenterology2024
Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors.
Article in World journal of gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate.Frontiers in oncology · 2026Article
- FAM83A may serve as a potential prognostic indicator for stomach adenocarcinoma.Translational cancer research · 2025Article
- NalbuphineWorld journal of gastrointestinal pharmacology and therapeutics · 2025Article
- Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study.BMC cancer · 2025Article
- Machine learning-based predictive model for immune checkpoint inhibitors response in gastrointestinal cancers.Frontiers in medicine · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundAlthough 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.
aimTo investigate biomarkers that predict prognosis in GC patients treated with ICIs and develop accurate predictive models.
methodsData 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.
resultsAmong 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.
conclusionThe 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.
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