Evidence map›Paper›PMID 38373959›Full record

ArticleJournal of translational medicine2024

DeepRisk network: an AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images.

Mengxin Tian, Zhao Yao, Yufu Zhou, Qiangjun Gan, Leihao Wang, Hongwei Lu, Siyuan Wang, Peng Zhou, Zhiqiang Dai, Sijia Zhang and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
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

13 citing papers in PubMed, 10 citations in OpenAlex.

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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

14 authors at 3 institutions in 1 country.

Mengxin Tian *Department of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
Zhao Yao *Biomedical Engineering Center, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Yufu Zhou *Department of Immunology and Pathogenic Biology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China.
Qiangjun Gan *Department of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
Leihao WangDepartment of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
Hongwei LuBiomedical Engineering Center, School of Information Science and Technology, Fudan University, Shanghai, 200433, China.
Siyuan WangDepartment of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
Peng ZhouDepartment of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
Zhiqiang DaiDepartment of General Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
Sijia ZhangDepartment of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
Yihong SunDepartment of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
Zhaoqing TangDepartment of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. tang.zhaoqing@zs-hospital.sh.cn.
Jinhua YuBiomedical Engineering Center, School of Information Science and Technology, Fudan University, Shanghai, 200433, China. jhyu@fudan.edu.cn.
Xuefei WangDepartment of Gastrointestinal Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. wang.xuefei@zs-hospital.sh.cn.ORCID 0000-0002-2645-0953
Fudan University · CNShanghai Medical College of Fudan University · CNShanghai University of Traditional Chinese Medicine · CN

Funding

National Natural Science Foundation of China 81902963National Natural Science Foundation of China 82273303
6 · The paper itself

Abstract

backgroundDigital histopathology provides valuable information for clinical decision-making. We hypothesized that a deep risk network (DeepRisk) based on digital pathology signature (DPS) derived from whole-slide images could improve the prognostic value of the tumor, node, and metastasis (TNM) staging system and offer chemotherapeutic benefits for gastric cancer (GC).

methodsDeepRisk is a multi-scale, attention-based learning model developed on 1120 GCs in the Zhongshan dataset and validated with two external datasets. Then, we assessed its association with prognosis and treatment response. The multi-omics analysis and multiplex Immunohistochemistry were conducted to evaluate the potential pathogenesis and spatial immune contexture underlying DPS.

resultsMultivariate analysis indicated that the DPS was an independent prognosticator with a better C-index (0.84 for overall survival and 0.71 for disease-free survival). Patients with low-DPS after neoadjuvant chemotherapy responded favorably to treatment. Spatial analysis indicated that exhausted immune clusters and increased infiltration of CD11b

conclusionDeepRisk network is a reliable tool that enhances prognostic value of TNM staging and aid in precise treatment, providing insights into the underlying pathogenic mechanisms.

Indexed as

AdenocarcinomaStomach NeoplasmsArtificial IntelligenceClinical Decision-MakingHumansNeoadjuvant TherapyPrognosisArtificial intelligenceDeep learningGastric cancerSuppressive immune microenvironmentWhole slide image

Identifiers

PMID38373959
PMCPMC10877826
OpenAlexW4391929441

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.