Evidence map›Paper›PMID 42098261›Full record

ArticleNPJ precision oncology2026

An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.

Jianxin Ji, Xuan Zhang, Menglei Hua, Meng Wang, Huiying Li, Xiaohan Zheng, Liuying Wang, Hesong Wang, Yongzhen Song, Jia He and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Jianxin JiDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Xuan ZhangDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Menglei HuaDepartment of Biomedical Engineering, School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China.
Meng WangDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Huiying LiDepartment of Pathology, Harbin Medical University Cancer Hospital, Harbin, China.
Xiaohan ZhengDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Liuying WangSchool of Health Management, Harbin Medical University, Harbin, China.
Hesong WangDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Yongzhen SongDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Jia HeDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Ruihao QinDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Yong CaoDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Qi ZhangDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Kaiyuan GeDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Yaru WangDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Huimin ZhangDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China.
Shenghan LouDepartment of Oncology Surgery, Harbin Medical University Cancer Hospital, Harbin, China. lou1219@126.com.
Peng HanDepartment of Oncology Surgery, Harbin Medical University Cancer Hospital, Harbin, China. leospiv@hrbmu.edu.cn.
Lei CaoDepartment of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China. caolei@hrbmu.edu.cn.

Funding

Harbin Medical University Cancer Hospital Ascend Leading Disciplines Plan PDYS-2024-14Heilongjiang Provincial Higher Education Institutions Collaborative Innovation Cultivation Project LJGXCG2023-087National Natural Science Foundation of China 82304250Natural Science Foundation of Heilongjiang Province LH2023H096Natural Science Foundation of Heilongjiang Province YQ2024H006the China Postdoctoral Science Foundation 2023MD744213the Postdoctoral research project in Heilongjiang Province LBHZ22210the Scientific research project of Heilongjiang Provincial Health Commission 20230404080339
6 · The paper itself

Abstract

Prognostic stratification in gastric cancer (GC) currently relies on the tumour-node-metastasis (TNM) staging system, which incompletely captures tumour heterogeneity. Routine haematoxylin and eosin (H&E)-stained whole-slide images (WSIs) contain additional prognostic information that is not routinely quantified. We developed an interpretable deep learning framework using a weakly supervised Transformer to derive a pathological risk score (TPRS) from WSIs for overall survival (OS) stratification and adjuvant chemotherapy benefit prediction. TPRS was developed on HMU-GC (n = 2876) and validated internally (n = 288) and on TCGA-STAD (n = 355). It achieved a mean 10-fold cross-validation C-index of 0.765 ± 0.003 internally and 0.621 ± 0.005 externally, and was an independent prognostic factor. Stage III patients with high TPRS showed significant survival benefit from adjuvant chemotherapy. Mediation analysis of differentially expressed genes (DEGs) and cellular features in high-attention patches supported a 'Gene → Cellular Features → TPRS' relationship, linking transcriptomics to cellular features and TPRS.

Identifiers

PMID42098261
PMCPMC13365203

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.