{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:00:56Z","timestamp":1790744456448,"version":"4.1.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Extensive-stage small cell lung cancer (ES-SCLC) is a rare yet highly aggressive malignancy with severely limited treatment options. The efficacy of immunotherapy for ES-SCLC remain limited, with only 14% of patients deriving additional benefit from immune checkpoint inhibitors (ICIs), underscoring significant heterogeneity in patient response. This study aimed to develop a model for the accurate prediction of prognosis and therapeutic response to optimize treatment strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>99 and 25 ES-SCLC patients were enrolled as training and independent internal validation cohorts. Baseline CT images, clinical data, and laboratory markers were collected. Prognostic factors were screened via Cox regression. A mediastinal window CT-based deep learning model (MWDL) was developed and combined with Progastrin-releasing peptide (log(ProGRP + 1)) and bone metastasis to establish the novel MWGB-Cox model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ProGRP and bone metastasis were independent prognostic factors with area under the curves (AUCs) of 0.705 and 0.666 respectively for predicting 1-year OS. Among the pure deep learning models, the MWDL model achieved better performance with a mean AUC of 0.813 than the deep learning model based on lung window CT images (LWDL, AUC 0.756). Whereas the multimodal MWGB-Cox model not only achieved the highest AUC (0.902) for 1-year OS prediction, but also effectively overcame the limitation of insufficient sensitivity. The predictive performance of the MWGB-Cox model was further validated in the independent internal validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This novel, simple, and cost-effective multimodal MWGB-Cox model integrating CT-based deep learning, log(ProGRP + 1), and bone metastasis could robustly predict 1-year OS of ES-SCLC patients receiving first-line immunotherapy, providing a reliable tool for clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>By integrating CT-based deep learning, log(ProGRP + 1) levels, and bone metastasis, the novel multimodal model achieved the highest predictive AUC (0.902) with improved sensitivity, serving as a simple, cost-effective tool for clinical decision-making.<\/jats:p>\n               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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liwen","family":"Gu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine","place":["Guangzhou, China"]},{"name":"Department of Critical Care Medicine, Sun Yat-sen University Cancer Center","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peixin","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baomo","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihua","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haohua","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Urology, Guangdong 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