Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer
Authors
Keywords
-
Journal
Nature Communications
Volume 13, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-11-09
DOI
10.1038/s41467-022-34275-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- FDA Approval Summary: Pembrolizumab, Atezolizumab, and Cemiplimab-rwlc as single agents for first-line treatment of advanced/metastatic PD-L1 high NSCLC
- (2022) Oladimeji Akinboro et al. CLINICAL CANCER RESEARCH
- Interobserver Variation of PD-L1 SP142 Immunohistochemistry Interpretation in Breast Carcinoma: A Study of 79 Cases Using Whole Slide Imaging
- (2021) Raza S. Hoda et al. ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
- Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods
- (2021) Huu-Giao Nguyen et al. Scientific Reports
- Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy
- (2021) Dmitrii Bychkov et al. Scientific Reports
- SP142 PD-L1 Scoring Shows High Interobserver and Intraobserver Agreement in Triple-negative Breast Carcinoma But Overall Low Percentage Agreement With Other PD-L1 Clones SP263 and 22C3
- (2021) Jia-Min B. Pang et al. AMERICAN JOURNAL OF SURGICAL PATHOLOGY
- Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma
- (2021) Behrus Puladi et al. Cancers
- PD-L1 Expression Is Significantly Associated with Tumor Mutation Burden and Microsatellite Instability Score
- (2021) Yoon Ah Cho et al. Cancers
- How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies
- (2021) Xinran Wang et al. npj Breast Cancer
- Prevalence and mutational determinants of high tumor mutation burden in breast cancer
- (2020) R. Barroso-Sousa et al. ANNALS OF ONCOLOGY
- A machine learning-based approach for the inference of immunotherapy biomarker status in lung adenocarcinoma from hematoxylin and eosin (H&E) histopathology images.
- (2020) Cory Batenchuk et al. JOURNAL OF CLINICAL ONCOLOGY
- Prospective multi-institutional evaluation of pathologist assessment of PD-L1 assays for patient selection in triple negative breast cancer
- (2020) Emily S. Reisenbichler et al. MODERN PATHOLOGY
- Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images
- (2020) Rishi R. Rawat et al. Scientific Reports
- Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
- (2020) Nikhil Naik et al. Nature Communications
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
- (2020) Erico Tjoa et al. IEEE Transactions on Neural Networks and Learning Systems
- Neoantigen screening identifies broad TP53 mutant immunogenicity in patients with epithelial cancers
- (2019) Parisa Malekzadeh et al. JOURNAL OF CLINICAL INVESTIGATION
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
- (2019) Gabriele Campanella et al. NATURE MEDICINE
- PD-1/PD-L1 Targeting in Breast Cancer: The First Clinical Evidences Are Emerging. A Literature Review
- (2019) Planes-Laine et al. Cancers
- Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis
- (2019) Moritz Widmaier et al. MODERN PATHOLOGY
- Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial
- (2019) Peter Schmid et al. LANCET ONCOLOGY
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Focal loss for dense object detection
- (2018) Tsung-Yi Lin et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Pembrolizumab monotherapy for previously untreated, PD-L1-positive, metastatic triple-negative breast cancer: cohort B of the phase II KEYNOTE-086 study
- (2018) S Adams et al. ANNALS OF ONCOLOGY
- Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies
- (2018) Ansh Kapil et al. Scientific Reports
- Automated Tumour Recognition and Digital Pathology Scoring Unravels New Role for PD-L1 in Predicting Good Outcome in ER-/HER2+ Breast Cancer
- (2018) Matthew P. Humphries et al. Journal of Oncology
- Prognostic value of stromal tumour infiltrating lymphocytes and programmed cell death-ligand 1 expression in breast cancer
- (2017) António Polónia et al. JOURNAL OF CLINICAL PATHOLOGY
- A Prospective, Multi-institutional, Pathologist-Based Assessment of 4 Immunohistochemistry Assays for PD-L1 Expression in Non–Small Cell Lung Cancer
- (2017) David L. Rimm et al. JAMA Oncology
- Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
- (2016) Kun-Hsing Yu et al. Nature Communications
- PD-L1 Expression Is Associated with Tumor FOXP3+ Regulatory T-Cell Infiltration of Breast Cancer and Poor Prognosis of Patient
- (2016) Zhenhua Li et al. Journal of Cancer
- PDL1 Regulation by p53 via miR-34
- (2015) Maria Angelica Cortez et al. JNCI-Journal of the National Cancer Institute
- PDL1 Regulation by p53 via miR-34
- (2015) Maria Angelica Cortez et al. JNCI-Journal of the National Cancer Institute
- Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
- (2011) A. H. Beck et al. Science Translational Medicine
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started