Evidence map›Paper›PMID 42649896›Full record

ArticleCancers2026

Artificial Intelligence-Assisted Prediction of Immunotherapy Benefit and Recurrence Risk Stratification in Gastric Cancer: A Single-Center Real-World Study.

Dou-Dou Li, Jie-Yun Zhang, Yi-Yang Zhang, Yu-Feng Yang, Jun-Xi Chen, Xi-Yuan Chen, Xi Chen, Zhi-Huang Hu, Hai-Xia Wu, Chen-Chen Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Dou-Dou LiSchool of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Jie-Yun ZhangDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
Yi-Yang ZhangSchool of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Yu-Feng YangSchool of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Jun-Xi ChenSchool of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Xi-Yuan ChenSchool of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Xi ChenShanghai Jiai Intelligence Co., Ltd., 3-173, 328 Tiantong Rd., Hongkou, Shanghai 200086, China.
Zhi-Huang HuDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.ORCID 0000-0002-6547-3832
Hai-Xia WuDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, Minhang District, Shanghai 201102, China.
Chen-Chen WangDepartment of Medical Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.ORCID 0000-0003-4930-3187

Funding

National Natural Science Foundation of China ID: 82303658
6 · The paper itself

Abstract

Immune checkpoint inhibitors targeting the PD-1 pathway have improved outcomes in advanced gastric cancer; however, substantial heterogeneity in treatment response remains. Current prediction strategies mainly rely on pretreatment biomarkers, which provide static assessments and may not capture the evolving interactions between tumor progression and host immunity. This study aimed to develop a dynamic prediction framework integrating longitudinal clinical trajectories and immune remodeling for continuous risk assessment during PD-1 inhibitor therapy.

Indexed as

dynamic predictiongastric cancerimmune remodelinglandmark analysisPD-1 inhibitor

Identifiers

PMID42649896
PMCPMC13510696

What OpenQuestion holds

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Registered trials

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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.