ArticleiScience2023
Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.Briefings in functional genomics · 2025Pooled it
- 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
- Multimodal radiopathomics model predicts postoperative metachronous liver metastasis 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
- A Pathomics-Based Prognostic Model for Disease-Free Survival in Resected Gastric Cancer.Cancers · 2026Article
- A novel multimodal combining radiomics and tumor-stroma ratio (TSR) improves diagnosis of gastric cancer peritoneal metastasis.Clinical & experimental metastasis · 2026Article
- A risk prediction model for metachronous peritoneal metastasis after radical gastrectomy in gastric cancer based on peripheral blood inflammatory markers and tumor pathological features.American journal of translational research · 2026Article
- Development and Validation of a Pathomics-Based Prognostic Model for Patients with Lung Adenocarcinoma Undergoing First-Line EGFR-TKI Therapy.Annals of surgical oncology · 2026Article
- Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Development of a dynamic prediction model with the inclusion of time-dependent inflammatory biomarker enhances recurrence prediction after curative surgery for stage II or III gastric cancer.Japanese journal of clinical oncology · 2025Article
- Review
- Article
- Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis.Frontiers in oncology · 2025Article
- Development and validation of a collagen signature to predict central lymph node metastasis in papillary thyroid cancer.Frontiers in endocrinology · 2025Article
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- Novel nomogram and risk stratification for peritoneal recurrence after curative resection in gastric cancer.Scientific reports · 2024Article
- Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor.NPJ precision oncology · 2024Article
- Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer.Clinical and experimental otorhinolaryngology · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients.American journal of translational research · 2024Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual's risk in peritoneal recurrence of GC with serosal invasion.
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Registered trials
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.