ArticleNature biomedical engineering2025
A multimodal machine learning model for the stratification of breast cancer risk.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Ten Years of Artificial Intelligence in Screening Mammography: A Systematic Review and Meta-Analysis of Diagnostic Accuracy and Clinical Implementation (Literature Published 2015-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence-based multimodal integration of ultrasound and digital breast tomosynthesis for breast-level risk classification.Precision clinical medicine · 2026Article
- Predicting ki-67 expression in breast cancer via transformer and multiple instance learning on DCE-MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.Nature communications · 2026Article
- A multimodal deep learning classifier for prediction of HER2-low expression in triple-negative breast cancer.BMC medical imaging · 2026Article
- Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Benign Share Benefit to Malignant: Balanced Mixing on Feature Space for Imbalanced Breast Cancer Classification.Bioengineering (Basel, Switzerland) · 2026Article
- An Efficient Cross-Modal Interaction and Dynamic Fusion Network for Multimodal Breast Ultrasound Diagnosis.Tomography (Ann Arbor, Mich.) · 2026Article
- Interpretable Whole-Breast Radiomic Biomarkers for Exploratory Assessment of HER2 + Breast Cancer in Digital Mammography.Journal of imaging informatics in medicine · 2026Article
- An ultrasound foundation model for the stratification of vision impairment and eye cancer risk.NPJ digital medicine · 2026Article
- CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.NPJ digital medicine · 2026Article
- A deep learning system for non-invasive breast cancer diagnosis with multimodal data.Nature biomedical engineering · 2026Article
- AI knowledge, attitudes, perceptions, and willingness to use AI among oncologists in China: a nationwide cross-sectional study.BMC medical education · 2026Article
- Foundation Model-Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi-Institutional Retrospective Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal artificial intelligence in breast cancer: towards integrated prediction and personalized care.European radiology · 2026Article
- Development and validation of a malignant risk prediction model for breast cystic-solid lesions based on clinical and ultrasound features.BMC women's health · 2026Article
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A General-Purpose Framework for Microvascular Reconstruction and Quantitative Analysis in Ultrasound Localization Microscopy.BME frontiers · 2026Article
- Development and evaluation of a deep incremental learning system for mammography image analysis.PloS one · 2026Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other clinical tasks. For the models to impact clinical practice, they ought to follow standard workflows, help interpret mammography and ultrasound data, evaluate clinical contextual information, handle incomplete data and be validated in prospective settings. Here we report the development and testing of a multimodal model leveraging mammography and ultrasound modules for the stratification of breast cancer risk based on clinical metadata, mammography and trimodal ultrasound (19,360 images of 5,216 breasts) from 5,025 patients with surgically confirmed pathology across medical centres and scanner manufacturers. Compared with the performance of experienced radiologists, the model performed similarly at classifying tumours as benign or malignant and was superior at pathology-level differential diagnosis. With a prospectively collected dataset of 191 breasts from 187 patients, the overall accuracies of the multimodal model and of preliminary pathologist-level assessments of biopsied breast specimens were similar (90.1% vs 92.7%, respectively). Multimodal models may assist diagnosis in oncology.
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