ArticleNature communications2020
Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers.
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Who cites it
132 citing papers in PubMed, 238 citations in OpenAlex.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Benchmarking Open-Source Pathology Foundation Models for Breast Cancer Biomarker Prediction from H&E Whole-Slide Images.Cancers · 2026Article
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 2026Article
- Pan-cancer Distant Metastasis Prediction Based on Graph Neural Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Enabling DCIS subtyping: leveraging foundation models for robust grading and molecular biomarker scoring.NPJ breast cancer · 2026Article
- Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer.NPJ digital medicine · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 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
- Survival risk prediction for patients with lung adenocarcinoma containing micropapillary components based on weakly supervised pathomics.Journal of thoracic disease · 2026Article
- HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.Diagnostic pathology · 2026Article
- Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.NPJ breast cancer · 2026Article
- Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.NPJ digital medicine · 2026Article
- A deep learning model for the diagnosis of gastric neuroendocrine carcinoma.Communications medicine · 2026Article
- Habitat imaging based on DCE-MRI for differentiating luminal and non-luminal subtypes of breast cancer: a two-center study.Frontiers in oncology · 2026Article
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Virtual immunohistochemistry for ER and PR prediction in breast cancer histopathology: a two-stage conditional generative framework.Frontiers in oncology · 2026Article
- Computational Pathology with Topological signatures and Visual Word Encoding.Research square · 2025Article
- ESPWA: a deep learning-enabled tool for precision-based use of endocrine therapy in resource-limited settings.bioRxiv : the preprint server for biology · 2025Article
- Prognostic role of Ki-67 in colorectal carcinoma: Development and evaluation of machine learning prediction models.World journal of clinical oncology · 2025Article
72 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied tissue for the targeted receptor, which highlights the presence of cellular surface antigens. This is an expensive, time-consuming process which introduces discordance in results due to variability in IHC preparation and pathologist subjectivity. In contrast, hematoxylin and eosin (H&E) staining-which highlights cellular morphology-is quick, less expensive, and less variable in preparation. Here we show that machine learning can determine molecular marker status, as assessed by hormone receptors, directly from cellular morphology. We develop a multiple instance learning-based deep neural network that determines ERS from H&E-stained whole slide images (WSI). Our algorithm-trained strictly with WSI-level annotations-is accurate on a varied, multi-country dataset of 3,474 patients, achieving an area under the curve (AUC) of 0.92 for sensitivity and specificity. Our approach has the potential to augment clinicians' capabilities in cancer prognosis and theragnosis by harnessing biological signals imperceptible to the human eye.
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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.