ArticleNPJ breast cancer2023
Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study.
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 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- 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
- Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.NPJ digital medicine · 2026Article
- AUPA: weakly supervised approach for streamlining breast cancer diagnostic workflow by WSI histological type classification for efficient IHC triage.Scientific reports · 2025Article
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma.NPJ precision oncology · 2025Article
- Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma.BJC reports · 2025Article
- Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy Benefit.medRxiv : the preprint server for health sciences · 2025Article
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging.NPJ precision oncology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2-4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008-2012 in the Carolina Breast Cancer Study. We leveraged deep learning to extract image information and trained a model to identify recurrence. Cross-validation accuracy for predicting recurrence was 62.4% [95% CI: 55.7, 69.1], similar to grade (65.8% [95% CI: 59.3, 72.3]) and ER status (66.3% [95% CI: 59.8, 72.8]). Interestingly, 70% (19/27) of early-recurrent low-intermediate grade tumors were identified by our image model. Relative to existing markers, image-based analyses provide complementary information for predicting early recurrence.
Identifiers
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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.