ArticleJournal for immunotherapy of cancer2021
Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images.
Article in Journal for immunotherapy of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
132 citing papers in PubMed, 3 syntheses or guidelines pooled it, 199 citations in OpenAlex.
- 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
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Noninvasive early identification of durable clinical benefit from immune checkpoint inhibition: a prospective multicenter study (NCT04566432).Signal transduction and targeted therapy · 2024Trial
- Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape.Human vaccines & immunotherapeutics · 2026Article
- Deep learning of CT imaging predicts PD-L1 expression and immunotherapy response in metastatic NSCLC: A multi-center study.Cancer letters · 2026Article
- Noninvasive prediction of ALK fusion status in non-small cell lung cancer by a machine learning model combining CT images and clinical information.Journal of thoracic disease · 2026Article
- CT-based deep learning predicts immunotherapy response in esophageal squamous cell cancer patients.Journal of thoracic disease · 2026Article
- A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.Insights into imaging · 2026Article
- Fusion of Clinical and Deep Learning Features for Predicting Pembrolizumab Monotherapy Response in Advanced Non-Small Cell Lung Cancer.Journal of clinical medicine · 2026Article
- A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images.Scientific reports · 2026Article
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Enhanced CT-based deep learning radiomics and biological correlations for predicting immunotherapy efficacy in advanced non-small cell lung cancer.Translational cancer research · 2026Article
- QVT score, a radiomic biomarker of vascular complexity, enables prognostication and monitoring of NSCLC immunotherapy.Journal for immunotherapy of cancer · 2026Article
- A multicenter multimodel habitat radiomics model for predicting immunotherapy response in advanced NSCLC.iScience · 2026Article
- Construction and validation of a CT-based radiomics-deep learning signature for non-invasive prediction of PD-L1 expression and immunotherapy outcomes in non-small cell lung cancer.Translational lung cancer research · 2026Article
- A multicenter study: habitat imaging and radiomics to guide precision and individualized surgical treatment in chronic osteomyelitis.BMC medical imaging · 2026Article
- Tumor Characterization Using [Pharmaceuticals (Basel, Switzerland) · 2026Article
- Predicting PD-L1 expression in advanced EGFR-mutant lung adenocarcinoma patients using NECT, CECT radiomics and clinical features.BMC cancer · 2026Article
72 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundCurrently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) experience a durable clinical benefit (DCB). According to NCCN guidelines, Programmed death-ligand 1 (PD-L1) expression status determined by immunohistochemistry (IHC) of biopsies is the only clinically approved companion biomarker to trigger the use of ICI therapy. Based on prior work showing a relationship between quantitative imaging and gene expression, we hypothesize that quantitative imaging (radiomics) can provide an alternative surrogate for PD-L1 expression status in clinical decision support.
methods
resultsThe PD-L1 DLS significantly discriminated between PD-L1 positive and negative patients (area under receiver operating characteristics curve ≥0.82 in the training, validation, and two external test cohorts). Importantly, the DLS was indistinguishable from IHC-derived PD-L1 status in predicting PFS and OS, suggesting the utility of DLS as a surrogate for IHC. A score generated by combining the DLS with clinical characteristics was able to accurately (C-indexes of 0.70-0.87) predict DCB, PFS, and OS in retrospective training, prospective testing and external validation cohorts.
conclusionHence, we propose DLS as a surrogate or substitute for IHC-determined PD-L1 measurement to guide individual pretherapy decisions pending in larger prospective trials.
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Registered trials
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.