ArticleScientific reports2023
Multiparameter prediction model of immune checkpoint inhibitors combined with chemotherapy for non-small cell lung cancer based on support vector machine learning.
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 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.Thoracic cancer · 2025Pooled it
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Artificial intelligence and machine learning in non-small cell lung cancer: the current state of the science on multi-omic applications.BMC medical research methodology · 2026Review
- A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.iScience · 2025Article
- Early prediction of immunotherapy efficacy for advanced NSCLC based on clinical and pre-treatment contrast-enhanced CT radiomics features.Frontiers in oncology · 2025Article
- Just how transformative will AI/ML be for immuno-oncology?Journal for immunotherapy of cancer · 2024Review
- Applications of CT-based radiomics for the prediction of immune checkpoint markers and immunotherapeutic outcomes in non-small cell lung cancer.Frontiers in immunology · 2024Review
- [Tumor microenvironment-specific CT radiomics signature for predicting immunotherapy response in non-small cell lung cancer].Nan fang yi ke da xue xue bao = Journal of Southern Medical UniversityArticle
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reliable predictive markers to identify which patients with advanced non-small cell lung cancer tumors (NSCLC) will achieve durable clinical benefit (DCB) for chemo-immunotherapy are needed. In this retrospective study, we collected radiomics and clinical signatures from 94 patients with advanced NSCLC treated with anti-PD-1/PD-L1 combined with chemotherapy from January 1, 2018 to May 31, 2022. Radiomics variables were extracted from pretreatment CT and selected by Spearman correlation coefficients and clinical features by Logistics regression analysis. We performed effective diagnostic algorithms principal components analysis (PCA) and support vector machine (SVM) to develop an early classification model among DCB and non-durable benefit (NDB) groups. A total of 26 radiomics features and 6 clinical features were selected, and then principal component analysis was used to obtain 6 principal components for SVM building. RC-SVM achieved prediction accuracy with AUC of 0.91 (95% CI 0.87-0.94) in the training set, 0.73 (95% CI 0.61-0.85) in the cross-validation set, 0.84 (95% CI 0.80-0.89) in the external validation set. The new method of RC-SVM model based on radiomics-clinical signatures provides a significant additive value on response prediction in patients with NSCLC preceding chemo-immunotherapy.
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