{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T01:19:52Z","timestamp":1788830392258,"version":"build-2803163510"},"reference-count":32,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"8","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec>\n            <jats:title>PURPOSE<\/jats:title>\n            <jats:p>This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non\u2013small cell lung cancer (NSCLC) using real-world data (RWD) and clinical trial data.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>MATERIALS AND METHODS<\/jats:title>\n            <jats:p>\n              Retrospective RWD of 1,829 patients with advanced NSCLC treated with PD-(L)1 ICIs were collected from 10 academic and community institutions in the United States and Europe. The RWD included data sets for discovery (Data Set A-Discovery, n = 1,173) and independent test (Data Set B, n = 458). A radiomic pipeline, containing a deep learning feature extractor and a survival model, generated the computed tomography (CT) response score (CTRS) applied to the pretreatment routine CT\/positron emission tomography (PET)-CT scan. An enhanced CTRS (eCTRS) also incorporated age, sex, treatment line, and lesion annotations. Performance was evaluated against progression-free survival (PFS) and overall survival (OS). Biomarker generalizability was further evaluated using a secondary analysis of a prospective clinical trial (ClinicalTrials.gov identifier:\n              <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.clinicaltrials.gov\/ct2\/show\/NCT02573259\">NCT02573259<\/jats:ext-link>\n              ) evaluating the PD-1 inhibitor sasanlimab in second or later line of treatment (Data Set C, n = 54).\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS<\/jats:title>\n            <jats:p>In RWD Test Data Set B, the CTRS identified patients with a high probability of response to ICI with a PFS hazard ratio (HR) of 0.46 (95% CI, 0.26 to 0.82) and an OS HR of 0.50 (95% CI, 0.28 to 0.92) in the first-line ICI monotherapy cohort, after adjustment for baseline covariates including the PD-L1 tumor proportion score. In Clinical Trial Data Set C, the CTRS demonstrated an adjusted PFS HR of 1.03 (95% CI, 0.43 to 2.47) and an OS HR of 0.33 (95% CI, 0.14 to 0.91). The CTRS and eCTRS outperformed traditional imaging biomarkers of lesion size in PFS and OS for RWD Test Data Set B and in OS for the Clinical Trial Data Set.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSION<\/jats:title>\n            <jats:p>The study developed and validated a deep learning radiomic biomarker using pretreatment routine CT\/PET-CT scans to identify ICI benefit in advanced NSCLC.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1200\/cci.24.00133","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T21:00:21Z","timestamp":1734123621000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":16,"title":["Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non\u2013Small Cell Lung 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