Decoding Treatment Response and Prognosis in Unresectable Locally Advanced Non-Small Cell Lung Cancer by Integrating Spatiotemporal Heterogeneity From 18F-FDG Positron Emission Tomography-Computed Tomography: A Multicenter Study.
Treatment 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 18F-FDG positron emission tomography (PET)-computed tomography (CT)-derived spatiotemporal heterogeneity model to predict treatment response and prognosis in unresectable LA-NSCLC, and to explore its biological underpinnings.
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.
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 (P < .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.
This study establishes a noninvasive spatiotemporal heterogeneity biomarker based on 18F-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.
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.
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 (P < .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.
This study establishes a noninvasive spatiotemporal heterogeneity biomarker based on 18F-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.