ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Spectral focused imaging enables enhanced colorectal adenoma detection: a multicenter, parallel randomized controlled trial.BMC medicine · 2026Trial
- Characterization of acquired capecitabine resistance in MKN-45 gastric cancer cells reveals preserved apoptotic sensitivity.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Article
- The Lymph Node Ratio as a Predictive Biomarker for Individualized Benefit from Adjuvant Chemotherapy in Gastric Cancer: A Retrospective Cohort and Causal Machine Learning Study.Journal of gastrointestinal cancer · 2026Article
- Pan-cancer prioritization of CCDC69 reveals an immune-enriched and therapeutically sensitive breast cancer phenotype.Discover oncology · 2026Article
- Dual-energy computed tomography-derived extracellular volume fraction and spectral quantitative parameters for predicting early recurrence after gastrectomy: insights from a multicenter study.Abdominal radiology (New York) · 2026Article
- Article
- Study of phosphorylated ribosomal protein S6 (pS6) in the clinical outcomes of patients undergoing hepatectomy for metastatic colorectal cancer.World journal of surgical oncology · 2026Article
- An LSCC-specific R-loop-related model predicts prognosis and neoadjuvant immunotherapy response and identifies EIF5A2-mediated tumor-immune crosstalk.World journal of surgical oncology · 2026Article
- Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.Scientific reports · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- A novel ferroptosis- and endoplasmic reticulum stress-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer.Discover oncology · 2026Article
- Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer.NPJ digital medicine · 2026Article
- Probiotic supplementation as a nutritional strategy for the prevention and management of sarcopenia in older adults.Frontiers in cellular and infection microbiology · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- A deep learning-based prognostic model for diffuse large B-cell lymphoma incorporating PET/CT imaging features.Frontiers in oncology · 2026Article
- Artificial intelligence-driven gastrointestinal functional assessment: multimodal imaging, digital biomarkers, and real-time monitoring.Frontiers in physiology · 2026Review
- AI-driven pathology in esophageal cancer: from early screening to precision prognostics.Frontiers in oncology · 2026Review
- An albumin-based immune-nutritional score for predicting complete response, organ preservation, and toxicity after neoadjuvant PD-1-based therapy in low rectal cancer.Frontiers in immunology · 2026Article
- Baseline thyroid function and treatment-emergent thyroid dysfunction predict pathological response and survival after neoadjuvant PD-1 inhibitor plus platinum-based chemotherapy in locally advanced gastric and gastroesophageal junction adenocarcinoma: a multicenter cohort study.Frontiers in endocrinology · 2026Article
- Metabolic syndrome as an independent predictor of response and survival in locally advanced gastric cancer treated with neoadjuvant immunochemotherapy.Frontiers in oncology · 2026Article
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17 authors.
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Abstract
Accurate prediction of early postoperative recurrence in locally advanced gastric cancer (LAGC) remains challenging due to tumor heterogeneity and limitations of traditional clinicopathological factors. This study aims to develop and validate an interpretable multimodal model for precise recurrence prediction. 1580 LAGC patients are enrolled from six Chinese medical centers and a multimodal fusion Risk Stratification Assessment (RSA) model integrating clinical, radiomic, and pathomic data is developed. Model performance is evaluated using internal, external, prospective, and public dataset validations. Transcriptome sequencing is conducted to elucidate biological mechanisms underlying recurrence. The RSA model significantly outperforms clinical-only, radiomic-only, and pathomic-only models in predicting early recurrence, achieving area under the curve (AUC) values of 0.903 in the training cohort, 0.902 in internal validation, and ranging from 0.884 to 0.889 in external validations. Stratification by the RSA model consistently identifies high-risk patients with significantly poorer five-year survival across all cohorts (all P<0.001). Transcriptomic analysis reveals that high-risk patients exhibit significant immune cell infiltration, increased expression of immune checkpoint molecules, and activation of immune-related pathways, including interferon signaling and the IL-6/JAK/STAT3 pathway. The integrated multimodal RSA model effectively predicts recurrence risk and prognosis in LAGC, enabling precise patient stratification and individualized postoperative management.
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