ArticleEuropean journal of nuclear medicine and molecular imaging2026
Integrated tumor habitat and metabolic hot spot displacement analysis on [¹⁸F]FDG PET/CT for predicting treatment response and progression in unresectable locally advanced NSCLC: a two-center competing risk study.
Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Time-to-Event Machine Learning Model Incorporating MRI-Derived Intratumoral Heterogeneity Score for Predicting Invasive Breast Cancer Recurrence: A Dual-Center Study.Technology in cancer research & treatmentArticle
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Abstract
purposeTo evaluate the predictive value of [¹⁸F]FDG PET/CT derived tumor habitats and metabolic hot spot displacement (MHSD) for treatment response and disease progression in patients with unresectable locally advanced non-small cell lung cancer (LA-NSCLC).
methodsThis retrospective two-center study included 403 patients with unresectable LA-NSCLC who underwent baseline [¹⁸F]FDG PET/CT before antitumor therapy. The cohort was randomly divided into training (n = 241, 60%), validation (n = 81, 20%), and held-out testing (n = 81, 20%) cohorts. Four metabolic-density habitats were delineated using the Otsu thresholding algorithm to generate a habitat score. MHSD and the edge proximity score (EPS) were quantified to describe the spatial localization of metabolic dominance. The habitat model was compared with whole-tumor radiomics and clinical PET/CT models by the area under the receiver operating characteristic curve (AUC). Independent predictors of radiographically confirmed progression were evaluated using Fine-Gray competing risk regression.
resultsThe habitat model achieved AUCs of 0.843 (95% confidence interval [CI], 0.789-0.895), 0.822 (95% CI, 0.725-0.904), and 0.847 (95% CI, 0.744-0.935) in the training, validation, and held-out testing cohorts, respectively, and outperformed the radiomics and clinical PET/CT models. In multivariable Fine-Gray analysis, elevated pro-gastrin-releasing peptide (subdistribution hazard ratio [sHR], 1.86; 95% CI, 1.23-2.81; P = 0.004), higher Habitat Score per 0.1-unit increase (sHR, 1.49; 95% CI, 1.31-1.70; P < 0.001), and EPS-SUVmax (sHR, 1.80; 95% CI, 1.13-2.88; P = 0.014) were independent predictors of radiographically confirmed progression.
conclusionIntegrated assessment of tumor habitats and MHSD on [¹⁸F]FDG PET/CT may improve treatment-response prediction and progression-risk stratification in unresectable LA-NSCLC. These imaging biomarkers provide prognostic information beyond conventional radiomics and clinical PET/CT indices, though their specific predictive utility across distinct therapeutic regimens warrants prospective validation.
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