ArticleSignal transduction and targeted therapy2024
Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data.
Article in Signal transduction and targeted therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- Biomaterial-driven remove-remodel-repair (3R) strategy for full-cycle gastric cancer management: bibliometric mapping, preclinical evidence, and a translational roadmap.Nanoscale advances · 2026Review
- Molecular classification and precision therapy in gastric cancer: Current advances and future perspectives (Review).Oncology reports · 2026Review
- Immunotherapy for Digestive System Cancers: Progress, Challenges, and Future Directions.Biomedicines · 2026Review
- [Reflections on surgical management strategies for locally advanced gastric cancer after neoadjuvant immunotherapy].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Risk Factors for Cardiovascular Disease: Epidemiology, Screening, Prevention, and Therapeutic Interventions.MedComm · 2026Review
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- Chinese consensus on first-line precision therapy for advanced gastric cancer.Innovation (Cambridge (Mass.)) · 2026Review
- Article
- Gastric Cancer Organoids: Mechanistic Insights, Drug Discovery, and Translational Advances in Precision Medicine.Journal of gastric cancer · 2026Review
- Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.Insights into imaging · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- RNA‑binding protein DAZAP1 promotes gastric cancer metastasis by enhancing NOTCH1 and JAG1 mRNA stability.International journal of oncology · 2026Article
- Predicting primary resistance to third-generation EGFR-TKIs in lung adenocarcinoma using a multisource cross-modal transformer model.NPJ precision oncology · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.World journal of gastrointestinal oncology · 2026Review
- Deep learning-based multimodal pathogenomics integration for precision cancer prognosis.Journal of translational medicine · 2026Article
- SLC39A10 drives M2 macrophage polarization and gastric cancer progression through the MAPK14(p38α) pathway.iScience · 2026Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
20 authors.
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Abstract
The sole use of single modality data often fails to capture the complex heterogeneity among patients, including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens, for the treatment of HER2-positive gastric cancer (GC). This modality deficit has not been fully considered in many studies. Furthermore, the application of artificial intelligence in predicting the treatment response, particularly in complex diseases such as GC, is still in its infancy. Therefore, this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive GC. We collected multi-modal data, comprising radiology, pathology, and clinical information from a cohort of 429 patients: 310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors immunotherapy. We introduced a deep learning model, called the Multi-Modal model (MuMo), that integrates these data to make precise treatment response predictions. MuMo achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined immunotherapy. Moreover, patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival (log-rank test, P < 0.05). These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer, but also the potential and clinical value of our model.
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