ArticleNature communications2022
Prognostic and predictive value of a pathomics signature in gastric cancer.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers.
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Who cites it
116 citing papers in PubMed.
- Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.Journal of cancer research and clinical oncology · 2024Trial
- A novel postoperative nomogram and risk classification system for individualized estimation of survival among patients with parotid gland carcinoma after surgery.Journal of cancer research and clinical oncology · 2023Trial
- Integration of objective nutritional status and tumor burden predicts 5-year survival after gastric cancer surgery: a multicenter real-world study.Surgical endoscopy · 2026Article
- Deep Learning-Based Multi-Cancer Analysis for Predicting Disease-Free Survival Across Multiple Cancer Types.Cancers · 2026Article
- Multimodal radiopathomics model predicts postoperative metachronous liver metastasis in gastric cancer.Nature communications · 2026Article
- Potentially Functional Variants in FCER1A and PLCG2, Two B Cell-Related Immune Genes Predict the Survival of Chinese Gastric Cancer Patients.Molecular carcinogenesis · 2026Article
- Article
- Radiomics in Gastric Cancer: Advancing Precision Medicine.Journal of gastric cancer · 2026Review
- Microbiome-host interactions in different pathomic subtypes of early-stage lung adenocarcinoma.BMC pulmonary medicine · 2026Article
- Development and validation of pathomics signature for predicting prognosis of advanced high-grade serous ovarian carcinoma patients after platinum-based chemotherapy.Scientific reports · 2026Article
- An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.NPJ precision oncology · 2026Article
- Development and external validation of a prognostic nomogram for cancer-specific survival in elderly patients with early-stage gastric cancer.Journal of gastrointestinal 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
- Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.BMC medical imaging · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Development, validation, and visualization of a novel nomogram for predicting clinical outcomes of postoperative cervical cancer patients.Scientific reports · 2026Article
- Survival risk prediction for patients with lung adenocarcinoma containing micropapillary components based on weakly supervised pathomics.Journal of thoracic disease · 2026Article
- A Pathomics-Based Prognostic Model for Disease-Free Survival in Resected Gastric Cancer.Cancers · 2026Article
- Graph convolutional network-derived pathomics and clinical integration for predicting chemoimmunotherapy response in advanced lung squamous cell carcinoma: a multicenter study.Journal of translational medicine · 2026Article
56 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current tumour-node-metastasis (TNM) staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer (GC). Pathomics, which is based on the development of digital pathology, is an emerging field that might improve clinical management. Herein, we propose a pathomics signature (PS
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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.