ArticleFrontiers in immunology2022
Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.
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 13 papers.
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Who cites it
13 citing papers in PubMed.
- Noninvasive early identification of durable clinical benefit from immune checkpoint inhibition: a prospective multicenter study (NCT04566432).Signal transduction and targeted therapy · 2024Trial
- Risk stratification of PD-L1 expression in non-small cell lung cancer: a predictive model for prognosis.Journal of thoracic disease · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- A New Risk Score Based on Lipid Indicators for Patients with Advanced Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Review
- Non-tumor-related prognostic factors for immunotherapy-chemotherapy or immunotherapy alone as first-line in advanced non-small cell lung cancer (NSCLC).Clinical and experimental medicine · 2024Article
- Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies.Cancers · 2023Review
- Mutant-allele dispersion correlates with prognosis risk in patients with advanced non-small cell lung cancer.Journal of cancer research and clinical oncology · 2023Article
- The artificial intelligence and machine learning in lung cancer immunotherapy.Journal of hematology & oncology · 2023Review
- Integrated investigation of the prognostic role of HLA LOH in advanced lung cancer patients with immunotherapy.Frontiers in genetics · 2022Article
- An enhanced genetic mutation-based model for predicting the efficacy of immune checkpoint inhibitors in patients with melanoma.Frontiers in oncology · 2022Article
- Editorial: Application of artificial intelligence in improving immunotherapeutic efficacy.Frontiers in pharmacology · 2022Article
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Authors and funding
12 authors.
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Abstract
Due to the complex mechanisms affecting anti-tumor immune response, a single biomarker is insufficient to identify patients who will benefit from immune checkpoint inhibitors (ICIs) treatment. Therefore, a comprehensive predictive model is urgently required to predict the response to ICIs. A total of 162 non-small-cell lung cancer (NSCLC) patients undergoing ICIs treatment from three independent cohorts were enrolled and used as training and test cohorts (training cohort = 69, test cohort1 = 72, test cohort2 = 21). Eight genomic markers were extracted or calculated for each patient. Ten machine learning classifiers, such as the gaussian process classifier, random forest, and support vector machine (SVM), were evaluated. Three genomic biomarkers, namely tumor mutation burden, intratumoral heterogeneity, and loss of heterozygosity in human leukocyte antigen were screened out, and the SVM_poly method was adopted to construct a durable clinical benefit (DCB) prediction model. Compared with a single biomarker, the DCB multi-feature model exhibits better predictive value with the area under the curve values equal to 0.77 and 0.78 for test cohort1 and cohort2, respectively. The patients predicted to have DCB showed improved median progression-free survival (mPFS) and median overall survival (mOS) than those predicted to have non-durable clinical benefit.
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