ArticleMedComm2024
Multimodal data fusion AI model uncovers tumor microenvironment immunotyping heterogeneity and enhanced risk stratification of breast cancer.
Article in MedComm, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Research on the construction of an AI diagnostic model for plus disease of retinopathy of prematurity based on cross-center fusion datasets.Frontiers in pediatrics · 2026Article
- Targeting MDSCs in cancer: emerging immunotherapeutic and metabolic strategies.Frontiers in immunology · 2026Review
- Spatiotemporal control of mitoribosome-mediated metabolic reprogramming in cancer: implications for heterogeneity and therapeutic targeting.Frontiers in cell and developmental biology · 2026Review
- Biomarker prediction of immunotherapy response in breast cancer: from single markers to multi-omics integration.NPJ breast cancer · 2025Review
- Global research trends and prospects related to telomerase within breast cancer: a bibliometric and visual analysis.Discover oncology · 2025Article
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
- Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Evaluating AI-driven precision oncology for breast cancer in low- and middle-income countries: a review of machine learning performance, genomic data use, and clinical feasibility.Frontiers in digital health · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the leading cancer among women, with a significant number experiencing recurrence and metastasis, thereby reducing survival rates. This study focuses on the role of long noncoding RNAs (lncRNAs) in breast cancer immunotherapy response. We conducted an analysis involving 1027 patients from Sun Yat-sen Memorial Hospital, Sun Yat-sen University, and The Cancer Genome Atlas, utilizing RNA sequencing and pathology whole-slide images. We employed unsupervised clustering to identify distinct lncRNA expression patterns and developed an AI-based pathology model using convolutional neural networks to predict immune-metabolic subtypes. Additionally, we created a multimodal model integrating lncRNA data, immune-cell scores, clinical information, and pathology images for prognostic prediction. Our findings revealed four unique immune-metabolic subtypes, and the AI model demonstrated high predictive accuracy, highlighting the significant impact of lncRNAs on antitumor immunity and metabolic states within the tumor microenvironment. The AI-based pathology model, DeepClinMed-IM, exhibited high accuracy in predicting these subtypes. Additionally, the multimodal model, DeepClinMed-PGM, integrating pathology images, lncRNA data, immune-cell scores, and clinical information, showed superior prognostic performance. In conclusion, these AI models provide a robust foundation for precise prognostication and the identification of potential candidates for immunotherapy, advancing breast cancer research and treatment strategies.
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What Socratic holds
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 Socratic graph.