ArticleCell reports. Medicine2025
Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.
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 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas.CNS neuroscience & therapeutics · 2026Article
- Evaluation of Camptothecin Through Computational and Experimental Approaches Targeting Membrane Receptors on Breast Cancer Cells for Potential Therapeutic Applications.International journal of molecular sciences · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- An interpretable breast cancer risk stratification model via multi-omics integration: multi-method development and cross-cohort validation.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Multidrug resistance in cancer: current understandings and future perspective.Molecular biomedicine · 2026Review
- Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study.Journal of the American Heart Association · 2026Article
- Cutting-edge advances in endocrine therapy for breast cancer (Review).Oncology letters · 2026Review
- Artificial intelligence in breast cancer: applications and advancements.Cancer biology & medicine · 2026Review
- A Pathomics-Based Prognostic Model for Disease-Free Survival in Resected Gastric Cancer.Cancers · 2026Article
- Applications of Metabolomics to the Clinical Management of Breast Cancer: New Perspectives for Diagnosis, Treatment and Prognosis.International journal of molecular sciences · 2026Review
- An online interpretable machine learning model for predicting cardiometabolic multimorbidity risk in patients with type 2 diabetes mellitus.Scientific reports · 2026Article
- A latent factor framework to organize regulatory and metabolic programs inferred from scRNA-seq.Bioinformatics advances · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- A dual-validated machine learning model for predicting 3-year mortality in atypical pulmonary carcinoid, a rare neuroendocrine tumor.Frontiers in endocrinology · 2026Article
- Exploring the recurrence and metastasis of breast invasive ductal carcinoma based on machine learning and survival analysis.Frontiers in oncology · 2026Article
- Multimodal Deep Learning with Routine Clinical Data for Recurrence Risk Stratification in HRResearch (Washington, D.C.) · 2026Article
- A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer.Cell reports. Medicine · 2025Article
- Analysis of factors affecting axillary lymph node metastasis in breast cancer and the establishment and validation of a predictive model.Scientific reports · 2025Article
- Integrating allostasis and emerging technologies to study complex diseases.Communications biology · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with continuous recurrence remaining an important clinical issue. Current relapse predictive models in HR+/HER2- breast cancer patients still have limitations. The integration of multidimensional data represents a promising alternative for predicting relapse. In this study, we leverage our multi-omics cohort comprising 579 HR+/HER2- breast cancer patients (200 patients with complete data across 7 modalities) and develop a machine-learning-based model, namely CIMPTGV, which integrates clinical information, immunohistochemistry, metabolomics, pathomics, transcriptomics, genomics, and copy number variations to predict recurrence risk of HR+/HER2- breast cancer. This model achieves concordance indices (C-indices) of 0.871 and 0.869 in the train and test sets, respectively. The risk population predicted by the CIMPTGV model encompasses those identified by single-modality models. Feature analysis reveals that synergistic and complementary effects exist in different modalities. Simultaneously, we develop a simplified model with a mean area under the curve (AUC) of 0.840, presenting a useful approach for clinical applications.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.