ArticleCell reports. Medicine2023
Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 3 of them syntheses that pooled it.
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Who cites it
99 citing papers in PubMed, 3 syntheses or guidelines pooled it, 101 citations in OpenAlex.
- Pooled it
- Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.Journal of translational medicine · 2025Pooled it
- Predictive performance of MRI and CT radiomics in predicting the response to induction chemotherapy in nasopharyngeal carcinoma: a network meta-analysis.Frontiers in oncology · 2025Pooled it
- Safety and efficacy of PD-1 inhibitor (sintilimab) combined with transarterial chemoembolization as the initial treatment in patients with intermediate-stage hepatocellular carcinoma beyond up-to-seven criteria.Journal for immunotherapy of cancer · 2025Trial
- Lymphocytic Reaction Combined with MRI Deep Learning Radiomics in Risk Stratification of Distant Metastases in Rectal Cancer.Cancers · 2026Article
- Hepatic stellate cells promote structural and functional remodeling of three-dimensional hepatocellular carcinoma coculture spheroids.Molecular biology reports · 2026Article
- Deep learning CT signature for predicting early liver metastases in pancreatic ductal adenocarcinoma.Nature communications · 2026Observational
- Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- A CT-based deep learning model with biological interpretation for predicting early recurrence after neoadjuvant therapy in gastric cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Radiomics in Gastric Cancer: Advancing Precision Medicine.Journal of gastric cancer · 2026Review
- The application of artificial intelligence in adrenal imaging: current state of knowledge, challenges, and future directions.Endocrine connections · 2026Article
- A transformer-based multimodal deep learning model for preoperative prediction of Ki-67 expression level in glioma.BMC medical imaging · 2026Article
- Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay.NPJ precision oncology · 2026Article
- Prediction of intravenous cyclophosphamide treatment efficacy in idiopathic inflammatory myopathies-associated interstitial lung disease patients: a nomogram based on radiomics and clinical factors.BMC pulmonary medicine · 2026Article
- Digital Pathology and the AI-Based Quantification of the Tumor Microenvironment in Gastrointestinal Cancer: From Tumor Budding and Tumor-Infiltrating Lymphocytes to Tertiary Lymphoid Structures.International journal of molecular sciences · 2026Review
- MRI-based radiomics explainable model for predicting recurrence of limb chronic osteomyelitis in limb bones treated by Masquelet technique.Bone & joint research · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- [Bottlenecks and breakthroughs in gastric cancer diagnosis and treatment: Towards a new era of precision and intelligent integration].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Review
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Review
39 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
18 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.
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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.