ArticleFrontiers in immunology2022
Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images.Scientific reports · 2026Article
- The role of BLZF1 in lung adenocarcinoma and its value as a diagnostic and prognostic biomarker.Scientific reports · 2026Article
- Independent Testing of Published CT Models for PD-L1 Status in Non-Small Cell Lung Cancer.Radiology · 2026Article
- A multicenter multimodel habitat radiomics model for predicting immunotherapy response in advanced NSCLC.iScience · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Integrative radiomics and habitat imaging models for predicting PD-L1 expression in non-small cell lung cancer.Frontiers in oncology · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Cross-Modality Learning for Predicting Immunohistochemistry Biomarkers from Hematoxylin and Eosin-Stained Whole Slide Images.The American journal of pathology · 2025Article
- The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment.International journal of molecular sciences · 2025Review
- Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.BMC medical imaging · 2025Article
- A review of enhanced biosignature immunotherapy tools for predicting lung cancer immune phenotypes using deep learning.Discover oncology · 2025Review
- China Protocol for early screening, precise diagnosis, and individualized treatment of lung cancer.Signal transduction and targeted therapy · 2025Review
- Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.Scientific reports · 2025Article
- Computed Tomography-Based Radiomics and Genomics Analyses for Survival Prediction of Stage III Unresectable Non-Small Cell Lung Cancer Treated With Definitive Chemoradiotherapy and Immunotherapy.Molecular carcinogenesis · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
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10 authors.
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Abstract
Background: Programmed death-ligand 1 (PD-L1) assessment of lung cancer in immunohistochemical assays was only approved diagnostic biomarker for immunotherapy. But the tumor proportion score (TPS) of PD-L1 was challenging owing to invasive sampling and intertumoral heterogeneity. There was a strong demand for the development of an artificial intelligence (AI) system to measure PD-L1 expression signature (ES) non-invasively. Methods: We developed an AI system using deep learning (DL), radiomics and combination models based on computed tomography (CT) images of 1,135 non-small cell lung cancer (NSCLC) patients with PD-L1 status. The deep learning feature was obtained through a 3D ResNet as the feature map extractor and the specialized classifier was constructed for the prediction and evaluation tasks. Then, a Cox proportional-hazards model combined with clinical factors and PD-L1 ES was utilized to evaluate prognosis in survival cohort. Results: The combination model achieved a robust high-performance with area under the receiver operating characteristic curves (AUCs) of 0.950 (95% CI, 0.938-0.960), 0.934 (95% CI, 0.906-0.964), and 0.946 (95% CI, 0.933-0.958), for predicting PD-L1ES <1%, 1-49%, and ≥50% in validation cohort, respectively. Additionally, when combination model was trained on multi-source features the performance of overall survival evaluation (C-index: 0.89) could be superior compared to these of the clinical model alone (C-index: 0.86). Conclusion: A non-invasive measurement using deep learning was proposed to access PD-L1 expression and survival outcomes of NSCLC. This study also indicated that deep learning model combined with clinical characteristics improved prediction capabilities, which would assist physicians in making rapid decision on clinical treatment options.
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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.