ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022
Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 2 of them syntheses that pooled it.
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Who cites it
81 citing papers in PubMed, 2 syntheses or guidelines pooled it, 153 citations in OpenAlex.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Antidepressant-Associated Treatment Emergent Mania: A Meta-Analysis to Guide Risk Modeling Pharmacogenomic Targets of Potential Clinical Value.Journal of clinical psychopharmacologyPooled it
- Artificial Intelligence-Powered Human Epidermal Growth Factor Receptor 2 and Tumor Microenvironment Analysis in Human Epidermal Growth Factor Receptor 2-Amplified Metastatic Colorectal Cancer: Exploratory Analysis of Phase II TRIUMPH Trial.JCO precision oncology · 2025Trial
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning.PLoS computational biology · 2026Article
- Pan-cancer atlas of cellular architecture reveals a nearest-neighbour distance-associated biomechanical-immune axis involving CD4+ memory T cells.Clinical and translational medicine · 2026Article
- Pan-cancer spatial atlas of tertiary lymphoid structures.Science (New York, N.Y.) · 2026Article
- Enabling DCIS subtyping: leveraging foundation models for robust grading and molecular biomarker scoring.NPJ breast cancer · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.NPJ digital medicine · 2026Article
- HER2 Heterogeneity in Breast Cancer: Biological Basis, Clinical Implications, and Therapeutic Strategies.Breast cancer (Dove Medical Press) · 2026Review
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Hierarchical Tissue-Specific Modeling of Pathology Images Predicts Response in HER2+ Breast Cancer.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- A multimodal framework to identify molecular mechanisms driving patient group-associated morphology through the integration of spatial transcriptomics and whole slide imaging.NPJ artificial intelligence · 2026Article
- A toolkit for generating virtual brightfield images of histological and immunohistochemical stains from multiplexed data with AI-based channel selection and image enhancement.Frontiers in bioinformatics · 2026Article
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- Cross-Modality Learning for Predicting Immunohistochemistry Biomarkers from Hematoxylin and Eosin-Stained Whole Slide Images.The American journal of pathology · 2025Article
- Morphological analysis of tumor microenvironment in HER2-positive breast cancer: predicting response to neoadjuvant chemotherapy on histopathological images.Breast cancer research : BCR · 2025Article
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
21 more citing papers are in PubMed but not listed here.
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7 authors at 3 institutions in 1 country.
Funding
Abstract
The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.
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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.