{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T20:53:13Z","timestamp":1790974393939,"version":"4.1.0"},"reference-count":30,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"10","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Precis Oncol"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      Treatment response in unresectable locally advanced non\u2013small cell lung cancer (LA-NSCLC) is highly variable due to marked intratumoral heterogeneity, while existing predictive biomarkers lack accuracy. We aimed to develop and validate a pretreatment\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG positron emission tomography (PET)-computed tomography (CT)\u2013derived spatiotemporal heterogeneity model to predict treatment response and prognosis in unresectable LA-NSCLC, and to explore its biological underpinnings.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>In a multicenter cohort of 434 patients with unresectable LA-NSCLC (training: n = 215; internal validation: n = 93; external validation: n = 126), treatment response was classified as responsive or nonresponsive based on initial assessment. Four distinct habitats (PEThigh-CThigh, PETlow-CTlow, PETlow-CThigh, and PEThigh-CTlow) were generated via the Otsu algorithm. Temporal heterogeneity was quantified using metabolic hotspot displacement (normalized distance from the hotspot to the tumor centroid and normalized distance from the hotspot to the tumor periphery) and integrated with spatial radiomics to build a multidimensional spatiotemporal model. Model performance was evaluated against conventional metrics, with interpretability enhanced via nomogram and SHapley Additive exPlanations analysis. Survival outcomes were assessed using Kaplan-Meier curves, and public transcriptomic data were analyzed to characterize underlying pathways.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The spatiotemporal model accurately predicted treatment response with AUCs of 0.822 (training), 0.821 (internal validation), and 0.810 (external validation), significantly outperforming conventional whole-tumor PET-CT models. It maintained robust generalizability across treatment subgroups and effectively stratified patients by progression-free survival and overall survival (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .01). PEThigh-CThigh habitat volume fraction correlated with treatment resistance, whereas PETlow-CTlow habitat correlated with better response. Exploratory pathway analysis highlighted key pathways involving drug metabolism, glycolytic reprogramming, and immune microenvironment remodeling.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study establishes a noninvasive spatiotemporal heterogeneity biomarker based on\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG PET-CT that reliably predicts treatment response and prognostic outcomes in unresectable LA-NSCLC. It offers valuable insights for personalized clinical decision making and supports precision oncology.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/po-26-00609","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T19:59:07Z","timestamp":1790971147000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Decoding Treatment Response and Prognosis in Unresectable Locally Advanced Non\u2013Small Cell Lung Cancer by Integrating Spatiotemporal Heterogeneity From\n                    <sup>18<\/sup>\n                    F-FDG Positron Emission Tomography-Computed Tomography: A Multicenter Study"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6078-0852","authenticated-orcid":true,"given":"Yu","family":"Ji","sequence":"first","affiliation":[{"name":"Department of Radiology, The Second Qilu Hospital of Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cancer Medical Center, People's Hospital of Chengwu County, Heze, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meixia","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaru","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Qilu Hospital of Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-7718","authenticated-orcid":true,"given":"Zhengjun","family":"Dai","sequence":"additional","affiliation":[{"name":"Institute of Research and Clinical Innovations, Neusoft Medical Systems Co. 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