Evidence map›Paper›PMID 41088294›Full record

ArticleJournal of translational medicine2025

Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer.

Taojun Zhang, Zepang Sun, Zhe Li, M Usman Ahmad, Md Tauhidul Islam, Fan Yang, Xianqi Yang, Lin Wu, Jingjing Xie, Wanying Feng and 7 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. 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

17 authors.

Taojun Zhang *Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, Guangdong, China.ORCID 0000-0002-8864-5705
Zepang Sun *Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, Guangdong, China.
Zhe Li *Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
M Usman AhmadDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
Md Tauhidul IslamDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Fan YangDepartment of Computer Science, Wake Forest University, Winston Salem, NC, USA.
Xianqi YangDepartment of Gastric Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Lin WuDepartment of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China.
Jingjing XieGraduate Group of Epidemiology, University of California Davis, Davis, CA, USA.
Wanying FengDepartment of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Yan ChenShenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
Alyssa A GuoDepartment of Internal Medicine, Wake Forest University School of Medicine, Winstm, on Sale, NC, USA.
Guoxin LiDepartment of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, Guangdong, China.
Wei WangDepartment of Gastric Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Jiang Yu *Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, Guangdong, China. balbc@163.com.
Zhenhui Li *Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China. lizhenhui@kmmu.edu.cn.
Yuming Jiang *Department of Radiation Oncology, Wake Forest University School of Medicine, North Carolina, Winston Salem, 27157, USA. yumjiang@wakehealth.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrent cancer staging methods cannot accurately predict survival outcomes and therapeutic benefits in cancer patients. Digital pathomics, a rapidly evolving field, holds significant potential to revolutionize disease evaluation.

methodsThis international multicenter study included 2463 patients with pan-gastrointestinal cancer from twelve cohorts of seven cancer centers, of whom 1653 patients were diagnosed with gastric cancer (GC). We proposed a deep learning pathomics signature (DLPS) by integrating information on three scales from the whole slide images (WSIs) of H&E-stained tissue, including pathomic features related to the nucleus, microenvironment, and single-cell spatial distribution. Next, we assessed the predictive accuracy of the DLPS for prognosis, chemotherapy response, and immunotherapy response.

resultsOur findings revealed that the DLPS was significantly associated with overall survival in gastric cancer (GC) and pan-gastrointestinal cancers, exhibiting good accuracy with area under the curve values ranging from 0.723 to 0.840 across GC cohorts. Multivariable Cox regression analyses indicated that the DLPS is an independent prognostic factor. The nomogram that integrated the DLPS and TNM stage showed improved performance in predicting cancer prognosis compared to that with TNM stage alone (P < 0.05). Importantly, GC patients with low-DLPS (but not those with high-DLPS) exhibited substantial benefits from adjuvant chemotherapy (P < 0.05). Furthermore, the objective response rate to anti-PD-1 immunotherapy was significantly higher in the low-DLPS group (29.6%) compared to the high-DLPS group (8.3%, P < 0.05).

conclusionsThe DLPS has the potential to enhance prognosis assessment and identify patients who are likely to benefit from adjuvant chemotherapy and immunotherapy in pan-gastrointestinal cancers, as well as in other solid tumors.

Indexed as

Computational BiologyGastrointestinal NeoplasmsImmunotherapyAgedDeep LearningFemaleHumansMaleMiddle AgedNeoplasm StagingNomogramsPrognosisProportional Hazards ModelsStomach NeoplasmsTreatment OutcomeChemotherapy benefitDeep learningImmunotherapy responseTumor microenvironmentWhole slide images

Identifiers

PMID41088294
PMCPMC12522295

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.