Evidence map›Paper›PMID 40914924›Full record

Observational studyGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2025

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.

Shulun Nie, Shuyi Song, Qian Xu, Xin Dai, Aina Liu, Meili Sun, Lei Cong, Jing Liang, Zimin Liu, Jing Lv and 9 more

Abstract readMulticenter StudyObservational StudyValidation Study
PubMed Publisher
In one paragraph

Observational study in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Shulun NieDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Shuyi SongDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Qian XuDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Xin DaiDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Aina LiuDepartment of Oncology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, China.
Meili SunDepartment of Oncology, Central Hospital Affiliated to Shandong First Medical University & Jinan Central Hospital, Jinan, Shandong, China.
Lei CongDepartment of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Jing LiangDepartment of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.
Zimin LiuDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Jing LvDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Zhen LiDepartment of Oncology, Linyi Cancer Hospital, Linyi, Shandong, China.
Jinling ZhangDepartment of Oncology, Linyi People's Hospital, Linyi, Shandong, China.
Fangli CaoDepartment of Medical Oncology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
Linli QuDepartment of Medical Oncology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
Haiyan LiuDepartment of Oncology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China.
Lu YueDepartment of Oncology, Qingdao Municipal Hospital, Qingdao, Shandong, China.
Yi ZhaiDepartment of Oncology, Zibo Center Hospital, Zibo, Shandong, China.
Song LiDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Lian LiuDepartment of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China. lianliu@sdu.edu.cn.ORCID 0000-0001-5868-4482

Funding

National Natural Science Foundation of China 82173305
6 · The paper itself

Abstract

backgroundImmune 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.

methodsThis 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.

resultsThis 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/ .

conclusionsThe 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.

Indexed as

Biomarkers, TumorImmune Checkpoint InhibitorsMachine LearningStomach NeoplasmsAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesBiomarkers, TumorImmune Checkpoint InhibitorsAdvanced gastric cancerClinical laboratory omicsDynamic changeEfficacyImmune checkpoint inhibitorMachine learning

Identifiers

PMID40914924

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.