ArticleJCO precision oncology2026
Decoding Treatment Response and Prognosis in Unresectable Locally Advanced Non-Small Cell Lung Cancer by Integrating Spatiotemporal Heterogeneity From
Article in JCO precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTreatment response in unresectable locally advanced non-small 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 MATERIALS AND
methodsIn 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.
resultsThe 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 (
conclusionThis study establishes a noninvasive spatiotemporal heterogeneity biomarker based on
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