ArticleCancer science2023
Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.
Article in Cancer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- 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
- Deep survival analysis in multimodal medical data: a parametric and probabilistic approach with competing risks.Scientific reports · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.NPJ precision oncology · 2026Article
- Cellular Origins and Context-Dependent Prognostic Effects of Lactate Metabolism Genes Reveal Novel Molecular Subtypes in Gastric Cancer.Current issues in molecular biology · 2026Article
- Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study.Journal of clinical medicine · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- The consensus molecular subtypes of esophageal squamous cell carcinoma.Signal transduction and targeted therapy · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- A Machine Learning Model Based on Clinical Factors to Predict the Efficacy of First-Line Immunochemotherapy for Patients With Advanced Gastric Cancer: Retrospective Study.JMIR medical informatics · 2025Article
- Predicting gastric cancer survival using machine learning: A systematic review.World journal of gastrointestinal oncology · 2025Article
- Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.BMC cancer · 2025Article
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images.Nature communications · 2025Article
- Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data.Briefings in bioinformatics · 2025Article
- MulitDeepsurv: survival analysis of gastric cancer based on deep learning multimodal fusion models.Biomedical optics express · 2025Article
- Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization.Scientific reports · 2024Article
- Artificial Intelligence in the Pathology of Gastric Cancer.Journal of gastric cancer · 2023Review
- Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.Cancer science · 2023Article
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Authors and funding
11 authors.
Funding
Abstract
Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/DeepCoxSC).
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