{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T19:06:09Z","timestamp":1788980769363,"version":"build-2803163510"},"reference-count":45,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Preoperative stratification for non\u2013small cell lung cancer (NSCLC) necessitates separate evaluations of lymph node metastasis (LNM) to guide surgical decisions and of programmed death-ligand 1 (PD-L1) expression to inform immunotherapy.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n \n This study aimed to develop and validate an integrated diagnostic solution that could simultaneously predict both LNM status and PD-L1 expression status from a single, standard-of-care\n 18<\/jats:sup>\n F-fluoro-2-deoxy-D-glucose positron emission tomography\/computed tomography (\n 18<\/jats:sup>\n F-FDG PET\/CT) scan.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this multicenter study, we segmented primary tumors and peritumoral 15-mm expansion regions from preoperative PET\/CT scans of 273 patients (for LNM prediction) and 242 patients (for PD-L1 prediction). A total of 7868 radiomic features from intratumoral and peritumoral regions were extracted. Following rigorous feature selection, 2 independent models were developed using machine learning and tested on a temporal validation cohort (n=45). Model performance was benchmarked against clinicopathological models and nuclear medicine physicians.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The integrated model for LNM prediction (PT-IPT-LR) achieved an area under the curve of 0.845 (95% CI 0.716\u20100.973) in the temporal validation cohort, with a sensitivity of 0.765 (95% CI 0.518\u20101.000) and a specificity of 0.786 (95% CI 0.602\u20100.970). The model for PD-L1 expression (PT-IPT-SVM) achieved an area under the curve of 0.776 (95% CI 0.641\u20100.911) in the temporal validation cohort, with a sensitivity of 0.800 (95% CI 0.609\u20100.991) and a specificity of 0.650 (95% CI 0.401\u20100.899). Decision curve analysis confirmed the clinical utility of both models. Critically, we found no significant correlation between the radiomic signatures of LNM and PD-L1, which validates our 2-model approach.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n We present a radiomics framework that noninvasively integrates prediction of LNM and PD-L1 from a single preoperative PET\/CT scan. This tool may enable preoperative stratification, potentially optimizing both surgical and systemic treatment planning for patients with NSCLC in a single step.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/86835","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T18:11:50Z","timestamp":1788977510000},"page":"e86835-e86835","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Lymph Node Metastasis and Programmed Death-Ligand 1 Prediction in Non\u2013Small Cell Lung Cancer From a Single PET\/CT Scan: Multicenter Radiomics 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