Shulun Nie, Shuyi Song, Qian Xu, Xin Dai, Aina Liu, Meili Sun, Lei Cong, Jing Liang, Zimin Liu, Jing Lv, Zhen Li, Jinling Zhang, Fangli Cao, Linli Qu, Haiyan Liu, Lu Yue, Yi Zhai, Song Li, Lian Liu
{"title":"利用外周血临床组学数据动态变化预测晚期胃癌免疫检查点抑制剂疗效的机器学习模型的开发和验证：一项回顾性多中心队列研究。","authors":"Shulun Nie, Shuyi Song, Qian Xu, Xin Dai, Aina Liu, Meili Sun, Lei Cong, Jing Liang, Zimin Liu, Jing Lv, Zhen Li, Jinling Zhang, Fangli Cao, Linli Qu, Haiyan Liu, Lu Yue, Yi Zhai, Song Li, Lian Liu","doi":"10.1007/s10120-025-01655-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Immune checkpoint inhibitors (ICIs) play a pivotal role in the treatment of advanced gastric cancer (GC). However, the biomarkers used to predict ICI efficacy are limited due to their reliance on single or static tumor characteristics. This study aims to develop a machine learning (ML) model that incorporates dynamic changes in clinlabomics data to optimize the predictive accuracy of ICI efficacy.</p><p><strong>Methods: </strong>This multicenter, retrospective study utilized nine ML to construct the model. Participants were further stratified into low-risk and high-risk groups based on the predicted efficacy of ICI. Kaplan-Meier survival curves and RNA-sequencing were used for differential analysis.</p><p><strong>Results: </strong>This study enrolled 377 patients with advanced GC who underwent first-line ICI treatment across eleven hospitals between January 2018 and May 2023. Among them, 220 patients from Qilu Hospital of Shandong University were selected for the development model. The remaining ten hospitals contributed to two external test cohorts. Ten dynamic clinlabomics features were identified. The XGBoost demonstrated optimal performance in predicting ICI response, achieving an AUC of 0.863 in the training cohort, and 0.790-0.842 in the validation and two external cohorts. Notably, the model exhibited strong predictive capabilities compared to single point-in-time and previously proposed model. In the subgroup analysis, the low-risk subtype demonstrated a significantly improved prognosis and exhibited characteristics of \"hot tumors\". A web tool was generated: https://ici-therapeutic-efficacy-predictor-ztwwfwek2uckbmhxlnsayq.streamlit.app/ .</p><p><strong>Conclusions: </strong>The dynamic clinlabomics model can effectively predict the ICI efficacy in advanced GC. The model was validated using multicenter data and provides new evidence to optimize treatment decisions.</p>","PeriodicalId":12684,"journal":{"name":"Gastric Cancer","volume":" ","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a machine learning model for predicting immune checkpoint inhibitor efficacy in advanced gastric cancer using dynamic changes in peripheral blood clinlabomics data: a retrospective multicenter cohort study.\",\"authors\":\"Shulun Nie, Shuyi Song, Qian Xu, Xin Dai, Aina Liu, Meili Sun, Lei Cong, Jing Liang, Zimin Liu, Jing Lv, Zhen Li, Jinling Zhang, Fangli Cao, Linli Qu, Haiyan Liu, Lu Yue, Yi Zhai, Song Li, Lian Liu\",\"doi\":\"10.1007/s10120-025-01655-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Immune checkpoint inhibitors (ICIs) play a pivotal role in the treatment of advanced gastric cancer (GC). However, the biomarkers used to predict ICI efficacy are limited due to their reliance on single or static tumor characteristics. This study aims to develop a machine learning (ML) model that incorporates dynamic changes in clinlabomics data to optimize the predictive accuracy of ICI efficacy.</p><p><strong>Methods: </strong>This multicenter, retrospective study utilized nine ML to construct the model. Participants were further stratified into low-risk and high-risk groups based on the predicted efficacy of ICI. Kaplan-Meier survival curves and RNA-sequencing were used for differential analysis.</p><p><strong>Results: </strong>This study enrolled 377 patients with advanced GC who underwent first-line ICI treatment across eleven hospitals between January 2018 and May 2023. Among them, 220 patients from Qilu Hospital of Shandong University were selected for the development model. The remaining ten hospitals contributed to two external test cohorts. Ten dynamic clinlabomics features were identified. The XGBoost demonstrated optimal performance in predicting ICI response, achieving an AUC of 0.863 in the training cohort, and 0.790-0.842 in the validation and two external cohorts. Notably, the model exhibited strong predictive capabilities compared to single point-in-time and previously proposed model. In the subgroup analysis, the low-risk subtype demonstrated a significantly improved prognosis and exhibited characteristics of \\\"hot tumors\\\". A web tool was generated: https://ici-therapeutic-efficacy-predictor-ztwwfwek2uckbmhxlnsayq.streamlit.app/ .</p><p><strong>Conclusions: </strong>The dynamic clinlabomics model can effectively predict the ICI efficacy in advanced GC. The model was validated using multicenter data and provides new evidence to optimize treatment decisions.</p>\",\"PeriodicalId\":12684,\"journal\":{\"name\":\"Gastric Cancer\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gastric Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10120-025-01655-1\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gastric Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10120-025-01655-1","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
Development and validation of a machine learning model for predicting immune checkpoint inhibitor efficacy in advanced gastric cancer using dynamic changes in peripheral blood clinlabomics data: a retrospective multicenter cohort study.
Background: Immune checkpoint inhibitors (ICIs) play a pivotal role in the treatment of advanced gastric cancer (GC). However, the biomarkers used to predict ICI efficacy are limited due to their reliance on single or static tumor characteristics. This study aims to develop a machine learning (ML) model that incorporates dynamic changes in clinlabomics data to optimize the predictive accuracy of ICI efficacy.
Methods: This multicenter, retrospective study utilized nine ML to construct the model. Participants were further stratified into low-risk and high-risk groups based on the predicted efficacy of ICI. Kaplan-Meier survival curves and RNA-sequencing were used for differential analysis.
Results: This study enrolled 377 patients with advanced GC who underwent first-line ICI treatment across eleven hospitals between January 2018 and May 2023. Among them, 220 patients from Qilu Hospital of Shandong University were selected for the development model. The remaining ten hospitals contributed to two external test cohorts. Ten dynamic clinlabomics features were identified. The XGBoost demonstrated optimal performance in predicting ICI response, achieving an AUC of 0.863 in the training cohort, and 0.790-0.842 in the validation and two external cohorts. Notably, the model exhibited strong predictive capabilities compared to single point-in-time and previously proposed model. In the subgroup analysis, the low-risk subtype demonstrated a significantly improved prognosis and exhibited characteristics of "hot tumors". A web tool was generated: https://ici-therapeutic-efficacy-predictor-ztwwfwek2uckbmhxlnsayq.streamlit.app/ .
Conclusions: The dynamic clinlabomics model can effectively predict the ICI efficacy in advanced GC. The model was validated using multicenter data and provides new evidence to optimize treatment decisions.
期刊介绍：
Gastric Cancer is an esteemed global forum that focuses on various aspects of gastric cancer research, treatment, and biology worldwide.
The journal promotes a diverse range of content, including original articles, case reports, short communications, and technical notes. It also welcomes Letters to the Editor discussing published articles or sharing viewpoints on gastric cancer topics.
Review articles are predominantly sought after by the Editor, ensuring comprehensive coverage of the field.
With a dedicated and knowledgeable editorial team, the journal is committed to providing exceptional support and ensuring high levels of author satisfaction. In fact, over 90% of published authors have expressed their intent to publish again in our esteemed journal.
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