ArticleNPJ breast cancer2018
Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens.
Article in NPJ breast cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- External validation of precisebreast, a digital prognostic test for predicting breast cancer recurrence, in an early-stage cohort from the Netherlands.Breast cancer research : BCR · 2025Article
- Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.Breast cancer research : BCR · 2025Article
- Characterisation of HER2-Driven Morphometric Signature in Breast Cancer and Prediction of Risk of Recurrence.Cancer medicine · 2025Article
- Extreme wrinkling of the nuclear lamina is a morphological marker of cancer.NPJ precision oncology · 2024Article
- AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis.NPJ precision oncology · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model.JMIR cancer · 2023Article
- Next-Generation Morphometry for pathomics-data mining in histopathology.Nature communications · 2023Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- Breast Cancer Dataset, Classification and Detection Using Deep Learning.Healthcare (Basel, Switzerland) · 2022Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Article
- Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer.JNCI cancer spectrum · 2021Article
- Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.Nature communications · 2020Article
- A deep learning model to predict RNA-Seq expression of tumours from whole slide images.Nature communications · 2020Article
- Article
- Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images.Journal of pathology informatics · 2020Article
- Higher order genomic organization and epigenetic control maintain cellular identity and prevent breast cancer.Genes, chromosomes & cancer · 2019Review
- Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this pilot study, we introduce a machine learning framework to identify relationships between cancer tissue morphology and hormone receptor pathway activation in breast cancer pathology hematoxylin and eosin (H&E)-stained samples. As a proof-of-concept, we focus on predicting clinical estrogen receptor (ER) status-defined as greater than one percent of cells positive for estrogen receptor by immunohistochemistry staining-from spatial arrangement of nuclear features. Our learning pipeline segments nuclei from H&E images, extracts their position, shape and orientation descriptors, and then passes them to a deep neural network to predict ER status. After training on 57 tissue cores of invasive ductal carcinoma (IDC), our pipeline predicted ER status in an independent test set of patient samples (AUC ROC = 0.72, 95%CI = 0.55-0.89,
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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.