ArticleCell reports. Medicine2025
TME-guided deep learning predicts chemotherapy and immunotherapy response in gastric cancer with attention-enhanced residual Swin Transformer.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global research hotspots and trends in the application of artificial intelligence in gastric cancer: a bibliometric analysis from 2005 to 2024.Frontiers in oncology · 2025Pooled it
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Nanomedicine in precision treatment of gastric cancer: current clinical landscape.Molecular biology reports · 2026Review
- Bidirectional Perceptual Multimodal Interaction Network Based on Contrastive Learning for Breast Cancer pCR Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- ICIsc: A Deep Learning Framework for Predicting Immune Checkpoint Inhibitor Response by Integrating scRNA-Seq and Protein Language Models.Bioengineering (Basel, Switzerland) · 2026Article
- Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy Decision-Making in Luminal Breast Cancer.Research (Washington, D.C.) · 2026Article
- Deep learning-based radiomics and pathomics for decoding tumor microenvironment and predicting immunotherapy outcomes in gastric cancer.Frontiers in immunology · 2026Review
- Therapeutic vulnerability shaped by the microenvironment: multi-omics and AI biomarkers for precision surgical planning in gastrointestinal tumors.Frontiers in cell and developmental biology · 2026Article
- Interpretable multimodal radiopathomics model predicting pathological complete response to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.Journal for immunotherapy of cancer · 2025Article
- Exploring the role of miR-221/222 in gastrointestinal cancers: implications for innovative therapeutic strategies.Cancer cell international · 2025Review
- Integrated single-cell and spatial transcriptomics combined with whole-exome sequencing reveal key hub genes and epithelial heterogeneity in bladder cancer.Frontiers in oncology · 2025Article
- Metabolic reprogramming in the post-metastatic tumor microenvironment: multi-omics insights into determinants of immunotherapy response.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adjuvant chemotherapy and immune checkpoint blockade exert quite durable anti-tumor responses, but the lack of effective biomarkers limits the therapeutic benefits. Utilizing multi-cohorts of 3,095 patients with gastric cancer, we propose an attention-enhanced residual Swin Transformer network to predict chemotherapy response (main task), and two predicting subtasks (ImmunoScore and periostin [POSTN]) are used as intermediate tasks to improve the model's performance. Furthermore, we assess whether the model can identify which patients would benefit from immunotherapy. The deep learning model achieves high accuracy in predicting chemotherapy response and the tumor microenvironment (ImmunoScore and POSTN). We further find that the model can identify which patient may benefit from checkpoint blockade immunotherapy. This approach offers precise chemotherapy and immunotherapy response predictions, opening avenues for personalized treatment options. Prospective studies are warranted to validate its clinical utility.
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Registered trials
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