ArticleNPJ precision oncology2023
AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.
Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
- A bottom-up initiated digital external quality assessment scheme for the state-of-the-art pathology in Sweden: reduced variability between pathology departments.Virchows Archiv : an international journal of pathology · 2026Article
- World Health Organization classification of tumours of the breast 6th edition 2026.Histopathology · 2026Review
- 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
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AUPA: weakly supervised approach for streamlining breast cancer diagnostic workflow by WSI histological type classification for efficient IHC triage.Scientific reports · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy Benefit.medRxiv : the preprint server for health sciences · 2025Article
- Pharmacologic management of HR+/HER2- mBC: a clinically oriented review.Frontiers in oncology · 2025Review
- Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence.Cancers · 2024Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.Cancers · 2024Review
Corrections and comments
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Authors and funding
17 authors at 4 institutions in 2 countries.
Funding
Abstract
Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.
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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.