ArticleNPJ breast cancer2021
Integrative multiomics-histopathology analysis for breast cancer classification.
Article in NPJ breast cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Patch-to-slide fusion deep learning model for histological diagnosis of early pregnancy loss including hydatidiform mole.NPJ digital medicine · 2026Article
- Toward Automating the Summarization of Cancer Pathology Reports Using Large Language Models to Improve Clinical Usability.JCO clinical cancer informatics · 2026Article
- Contrastive learning enhances fairness in pathology artificial intelligence systems.Cell reports. Medicine · 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
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights.Clinical Medicine Insights. Oncology · 2025Review
- Cancer genomics and bioinformatics in Latin American countries: applications, challenges, and perspectives.Frontiers in oncology · 2025Review
- Image analysis-based identification of high risk ER-positive, HER2-negative breast cancers.Breast cancer research : BCR · 2024Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Article
- A systematic analysis of deep learning in genomics and histopathology for precision oncology.BMC medical genomics · 2024Review
- Image-guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake.Clinical and translational medicine · 2024Article
- Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma.Med (New York, N.Y.) · 2023Article
- The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer.International journal of molecular sciences · 2023Review
- Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.Nature communications · 2023Article
- Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation.Journal of pathology informatics · 2023Article
- Molecular mechanisms of multi-omic regulation in breast cancer.Frontiers in oncology · 2023Review
- Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review.Journal of personalized medicine · 2022Review
- Deep learning models for histologic grading of breast cancer and association with disease prognosis.NPJ breast cancer · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hematoxylin-and-eosin-stained slides to examine the relations between visual morphological signal, clinical subtyping, gene expression, and mutation status in breast cancer. We first designed fully automated models for tumor detection and pathology subtype classification, with the results validated in independent cohorts (area under the receiver operating characteristic curve ≥ 0.950). Using only visual information, our models achieved strong predictive performance in estrogen/progesterone/HER2 receptor status, PAM50 status, and TP53 mutation status. We demonstrated that these models learned lymphocyte-specific morphological signals to identify estrogen receptor status. Examination of the PAM50 cohort revealed a subset of PAM50 genes whose expression reflects cancer morphology. This work demonstrates the utility of deep learning-based image models in both clinical and research regimes, through its ability to uncover connections between visual morphology and genetic statuses.
Identifiers
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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.