{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T19:10:13Z","timestamp":1792696213272,"version":"4.5.0"},"reference-count":58,"publisher":"American Medical Association (AMA)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n IMPORTANCE<\/jats:title>\n Reliable early response biomarkers for overall survival (OS) are lacking for patients receiving immune checkpoint inhibitor (ICI) therapy for advanced non\u2013small cell lung cancer (NSCLC). Existing imaging-based measures, such as Response Evaluation Criteria in Solid Tumors (RECIST) and tumor volume change (TVC), have limited predictive value for long-term outcomes, and advanced imaging-based biomarkers may enhance decision-making in clinical practice and clinical trials.<\/jats:p>\n <\/jats:sec>\n \n OBJECTIVE<\/jats:title>\n To develop and validate a fully automated deep-learning imaging-based biomarker using pretherapy and 12-week follow-up computed tomography (CT) scans.<\/jats:p>\n <\/jats:sec>\n \n DESIGN, SETTING, AND PARTICIPANTS<\/jats:title>\n This prognostic study used retrospectively collected HER data from routine clinical practice (RCP) and clinical trial data from 2013 to 2023. A model using serial CT scans, Serial CT response score (Serial CTRS) was developed using a RCP discovery dataset, validated with 10 US and European institution RCP test datasets, and independently validated on a multinational clinical phase 1 trial of dostarlimab (GARNET). Participants were adults with advanced NSCLC starting ICIs in the period from 2013 to 2021 (RCP discovery), from 2013 to 2022 (RCP test), or from 2017 to 2018 (GARNET).<\/jats:p>\n <\/jats:sec>\n \n INTERVENTIONS<\/jats:title>\n ICI monotherapy or combination therapy in the first-line or later-line setting.<\/jats:p>\n <\/jats:sec>\n \n MAIN OUTCOMES AND MEASURES<\/jats:title>\n Cox proportional hazards regression and receiver operating characteristic-area under the curve modeled associations between Serial CTRS and OS.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n The study included 1830 patients (RCP discovery, 1171 patients; RCP test, 605 patients; GARNET, 54 patients) with a median (IQR) age of 67 (19-95) years; 1000 participants were male (55%), and 830 were female (45%). Serial CTRS was associated with OS in multivariable analysis controlling for age, sex, programmed death-ligand 1 expression, histologic profile, and tumor volume (hazard ratio [HR] for 10%-point higher probability of 12-month OS, 0.74 [95% CI, 0.70-0.79] for RCP test; 0.45 [95% CI, 0.32-0.65] for GARNET). Serial CTRS outperformed RECIST and TVC in OS risk discrimination, with higher HRs distinguishing low-survival and high-survival groups in RCP test (HR, 6.19; 95% CI, 4.12-9.28) and GARNET (HR, 18.00; 95% CI, 5.40-59.97). Predictive value persisted across programmed death-ligand 1 and RECIST subgroups, including stable disease.<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS AND RELEVANCE<\/jats:title>\n In this prognostic study of patients with advanced NSCLC receiving ICI treatment, the fully automated biomarker Serial CTRS predicted OS more effectively than resource-intensive RECIST and TVC measurements using the same scans.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.55759","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T16:31:15Z","timestamp":1769617875000},"page":"e2555759","source":"Crossref","is-referenced-by-count":10,"title":["Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non\u2013Small Cell Lung Cancer"],"prefix":"10.1001","volume":"9","author":[{"given":"Chiharu","family":"Sako","sequence":"first","affiliation":[{"name":"Onc.AI, San Carlos, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brenda F.","family":"Kurland","sequence":"additional","affiliation":[{"name":"GSK, Collegeville, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taly G.","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Onc.AI, San Carlos, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"He","sequence":"additional","affiliation":[{"name":"GSK, San Francsico, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marius","family":"De Groot","sequence":"additional","affiliation":[{"name":"Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King\u2019s College London, London, United Kingdom"},{"name":"GSK, Stevenage, United Kingdom"},{"name":"Groover Consulting, Rotterdam, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arpan A.","family":"Patel","sequence":"additional","affiliation":[{"name":"James P. 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