ArticleEBioMedicine2021
Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.
Article in EBioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 4 of them syntheses that pooled it.
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Who cites it
79 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Global research hotspots and trends in the application of artificial intelligence in gastric cancer: a bibliometric analysis from 2005 to 2024.Frontiers in oncology · 2025Pooled it
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
- Multi-Sequence Fusion MRI Deep Learning Models for Discrimination of Recurrent Nasopharyngeal Carcinoma and Osteoradionecrosis.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for StagingJournal of medical Internet research · 2026Article
- Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study.British journal of cancer · 2026Article
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Inflammatory marker-driven deep learning model for postoperative gastric cancer prognosis.BMC medical informatics and decision making · 2026Article
- Artificial intelligence for the prediction of prognosis in colorectal cancer patients using routine blood indices.NPJ digital medicine · 2026Article
- Single-Cell sequencing investigation of endoplasmic reticulum stress-related genes in gastric cancer prognostic models and identification of NOX5 as a novel therapeutic target.Translational oncology · 2026Article
- Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study.Journal of clinical medicine · 2026Article
- A deep learning-based digital biopsy for predicting early recurrence in gastric cancer.Nature communications · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study.Scientific reports · 2026Article
- A deep learning model for the diagnosis of gastric neuroendocrine carcinoma.Communications medicine · 2026Article
- Innovative pathological and therapeutic approaches for poorly cohesive gastric cancer.Frontiers in oncology · 2026Review
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Recent advances, comparative performance, and data requirements of artificial intelligence-assisted technologies for the diagnosis of gastric cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Review
19 more citing papers are in PubMed but not listed here.
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Authors and funding
16 authors.
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Abstract
backgroundTo reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.
methods2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC.
findingsThe discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/).
interpretationOur study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients.
fundingNot applicable.
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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.