ArticleNature communications2024
Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 15 citations in OpenAlex.
- Predicting PD-L1 + CD8 - status in non-small cell lung cancer tissue from clinical indicators using machine learning.Chinese medical journal · 2026Article
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Dynamic reprogramming of the tumor immune network via multicycle checkpoint degradation for cancer immunotherapy.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mucin phenotype-based deep learning framework for intestinal metaplasia-carcinogenesis progression prediction.NPJ precision oncology · 2025Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Predicability of PD-L1 expression in cancer cells based solely on H&E-stained sections.Journal of pathology informatics · 2025Review
- A pathomics-integrated multimodal model to evaluate chemoimmunotherapy efficacy in unresectable intrahepatic cholangiocarcinoma.JHEP reports : innovation in hepatology · 2025Article
- Artificial Intelligence-driven image analysis for standardised programmed death-ligand 1 expression evaluation in non-small cell lung cancer.Diagnostic pathology · 2025Article
- Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach.NPJ precision oncology · 2025Article
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Article
- Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.Frontiers in immunology · 2025Article
- Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides.Nature communications · 2024Article
- Multi-omics based artificial intelligence for cancer research.Advances in cancer research · 2024Review
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.
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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.