ArticleScientific reports2023
Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline 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
- Deep learning of CT imaging predicts PD-L1 expression and immunotherapy response in metastatic NSCLC: A multi-center study.Cancer letters · 2026Article
- Development and validation of a machine learning model for predicting objective response to PD-1 inhibitors in lung cancer patients.Translational lung cancer research · 2026Article
- CT-based radiomics as a non-invasive virtual biopsy for high PD-L1 expression prediction in non-small cell lung cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.Scientific reports · 2026Article
- Advances in Multi-Modal Biomarkers for Immunotherapy Response in Non-Small Cell Lung Cancer: ctDNA, Microbiome, and Radiomics.Cancers · 2026Review
- Independent Testing of Published CT Models for PD-L1 Status in Non-Small Cell Lung Cancer.Radiology · 2026Article
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- Review
- Artificial Intelligence-driven image analysis for standardised programmed death-ligand 1 expression evaluation in non-small cell lung cancer.Diagnostic pathology · 2025Article
- Multimodal radiomics fusion for predicting postoperative recurrence in NSCLC patients.Journal of cancer research and clinical oncology · 2025Article
- Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients.Journal of translational medicine · 2025Article
- Article
- Current status and progress of PD-L1 detection: guiding immunotherapy for non-small cell lung cancer.Clinical and experimental medicine · 2024Review
- An explainable long short-term memory network for surgical site infection identification.Surgery · 2024Article
- Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours.Nature reviews. Cancer · 2024Review
- Article
- Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics.Journal of translational medicine · 2024Article
- Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinoma: a two-center study.Frontiers in oncology · 2024Article
- Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.JTO clinical and research reports · 2023Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.
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