ArticleNPJ breast cancer2022
Deep learning models for histologic grading of breast cancer and association with disease prognosis.
Article in NPJ breast cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Improving cellular nuclei detection in histological images by combining super-resolution techniques and deep learning-based object detectors.Medical & biological engineering & computing · 2026Article
- A simple morphometric algorithm based on nuclear atypia features of invasive breast carcinoma: relationship with nuclear grade, Ki-67 level, and hormone receptor status.Breast cancer (Tokyo, Japan) · 2026Article
- Risk Factors Influencing Efficacy and Prognosis Evaluation of Neoadjuvant Systemic Therapy in Triple-Positive Breast Cancer.The Kaohsiung journal of medical sciences · 2026Article
- Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.Journal of pathology informatics · 2026Article
- Monocyte distribution width in breast cancer: Results from a retrospective exploratory pilot study.Molecular and clinical oncology · 2026Article
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Evaluating the prognostic significance of HIST1H4C in breast cancer: implications for neoadjuvant therapy.Scientific reports · 2026Article
- Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers.Current oncology (Toronto, Ont.) · 2026Review
- A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur.BMC medical imaging · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.Science advances · 2025Article
- Article
- Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.Nature communications · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- Predicting Nottingham grade in breast cancer digital pathology using a foundation model.Breast cancer research : BCR · 2025Article
- Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer.Breast cancer research : BCR · 2025Article
- Comparative analysis of convolutional neural networks and transformer architectures for breast cancer histopathological image classification.Frontiers in medicine · 2025Article
- From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.Nature protocols · 2025Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
Corrections and comments
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Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.
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Registered trials
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