ArticleNPJ breast cancer2023
Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence.
Article in NPJ breast cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- Article
- Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence.medRxiv : the preprint server for health sciences · 2026Article
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 2026Article
- Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Accessible Clinical Tool for Prognosis and Chemotherapy Prediction in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer in Diverse and Low-Resource Settings.JCO global oncology · 2026Article
- A distributed fusion framework for breast cancer recurrence prediction using MapReduce.Scientific reports · 2026Article
- Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study.The Lancet. Oncology · 2026Article
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- BPMambaMIL: A bio-inspired prototype-guided multiple instance learning for oncotype DX risk assessment in histopathology.Computer methods and programs in biomedicine · 2025Article
- Impact of anthracyclines in genomic high-risk, node-negative, HR-positive/HER2-negative breast cancer.Annals of oncology : official journal of the European Society for Medical Oncology · 2025Article
- Review
- Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images.Scientific reports · 2025Article
- Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer.medRxiv : the preprint server for health sciences · 2025Article
- Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.Emerging topics in life sciences · 2025Article
- Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.Nature communications · 2025Article
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma.NPJ precision oncology · 2025Article
- Towards precision therapy in HER2-positive early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Article
- Article
- Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma.BJC reports · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.83 versus 0.76 in an external validation cohort, p = 0.0005) and can identify a subset of patients with excellent prognoses who may not need further genomic testing.
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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.