ArticleNature communications2022
Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 1 of them a synthesis that pooled it.
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119 citing papers in PubMed, 1 synthesis or guideline pooled it, 170 citations in OpenAlex.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Predicting PD-L1 + CD8 - status in non-small cell lung cancer tissue from clinical indicators using machine learning.Chinese medical journal · 2026Article
- Identifying breast cancer subtypes and exploring spatial expression patterns of the key subtype-specific gene based on pathological images, single-cell sequencing, and spatial transcriptomic data.Breast cancer (Tokyo, Japan) · 2026Article
- Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- CT-based deep learning predicts immunotherapy response in esophageal squamous cell cancer patients.Journal of thoracic disease · 2026Article
- SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.Nature communications · 2026Article
- A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.Insights into imaging · 2026Article
- Review
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 2026Article
- SpaHE-Infil: A spatial heterogeneity framework for decoding TME infiltration from H&E-stained slides.iScience · 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
- Deep Learning-Based Tumor Cell Classification in Lung Adenocarcinoma With a Case-by-Case Human-in-the-Loop Approach.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Article
- Survival risk prediction for patients with lung adenocarcinoma containing micropapillary components based on weakly supervised pathomics.Journal of thoracic disease · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Thyroid cancer detection and classification using spectral imaging and artificial intelligence.Scientific reports · 2026Article
- Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
59 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxylin and eosin (H&E) is a robust staining used routinely for cancer diagnosis. Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques. With the help of two expert pathologists and a designed annotation software, we construct a dataset to assess the feasibility of PD-L1 prediction from H&E in breast cancer. In a cohort of 3,376 patients, our system predicts the PD-L1 status in a high area under the curve (AUC) of 0.91 - 0.93. Our system is validated on two external datasets, including an independent clinical trial cohort, showing consistent prediction performance. Furthermore, the proposed system predicts which cases are prone to pathologists miss-interpretation, showing it can serve as a decision support and quality assurance system in clinical practice.
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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.