ArticleCell reports. Medicine2022
Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images.
Article in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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49 citing papers in PubMed, 71 citations in OpenAlex.
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- World Health Organization classification of tumours of the breast 6th edition 2026.Histopathology · 2026Review
- Deep learning analysis of breast cancer histology predicts ATM pathogenic variant carrier status.NPJ digital medicine · 2026Article
- MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.Journal of the National Cancer Center · 2026Article
- Cross-cancer homologous recombination deficiency prediction from whole slide images using transfer learning.Scientific reports · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Pan-Cancer Prediction of Genomic Alterations from H&E Whole-Slide Images in a Real-World Clinical Cohort.Genes · 2026Article
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Clinical application of PARP inhibitors and emerging strategies to overcome resistance: a pan-cancer perspective.Biomarker research · 2026Review
- Predicting homologous recombination deficiency and treatment responses using a computed tomography-based foundation model: a preclinical study.NPJ precision oncology · 2026Article
- Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology.JHEP reports : innovation in hepatology · 2026Article
- HRD-Informed Digital Histology Model for Predicting Platinum Chemo-Response and Prognosis in High-Grade Serous Ovarian Cancer.Interdisciplinary sciences, computational life sciences · 2026Article
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Assessing genotype-phenotype correlations in colorectal cancer with deep learning: a multicentre cohort study.The Lancet. Digital health · 2025Article
- An Explainable Multimodal Artificial Intelligence Model Integrating Histopathological Microenvironment and EHR Phenotypes for Germline Genetic Testing in Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer.NPJ precision oncology · 2025Article
- Article
- Corr-A-Net: Interpretable Attention-Based Correlated Feature Learning framework for predicting of HER2 Score in Breast Cancer from H&E Images.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
10 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Homologous recombination DNA-repair deficiency (HRD) is becoming a well-recognized marker of platinum salt and polyADP-ribose polymerase inhibitor chemotherapies in ovarian and breast cancers. While large-scale screening for HRD using genomic markers is logistically and economically challenging, stained tissue slides are routinely acquired in clinical practice. With the objectives of providing a robust deep-learning method for HRD prediction from tissue slides and identifying related morphological phenotypes, we first show that digital pathology workflows are sensitive to potential biases in the training set, then we propose a method to overcome the influence of these biases, and we develop an interpretation method capable of identifying complex phenotypes. Application to our carefully curated in-house dataset allows us to predict HRD with high accuracy (area under the receiver-operator characteristics curve 0.86) and to identify morphological phenotypes related to HRD. In particular, the presence of laminated fibrosis and clear tumor cells associated with HRD open new hypotheses regarding its phenotypic impact.
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