ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026
A clinical-radiomic model based on best subset regression for prognostic prediction in natural killer/T-cell lymphoma.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 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
backgroundThe outcomes of patients with natural killer/T-cell lymphoma (NKTCL) are heterogeneous, thus, this study aimed to develop and validate prognostic models for overall survival (OS) and progression-free survival (PFS) in NKTCL by integrating clinicopathological and pre-treatment [18F]-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT)-derived variables, comparing the performance of univariate COX, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) methods.
methodsThis retrospective study analyzed a cohort of 114 pre-treatment patients with NKTCL who have performed [18F]-FDG PET/CT scan. Predictors included clinical parameters (e.g., age, albumin (Alb)), pathological indices (e.g., Ki67), Epstein-Barr virus (EBV), and imaging metrics (e.g., radiomics score (Radscore), maximum standardized uptake value (SUV
resultsThe BSR-derived model demonstrated superior performance for predicting OS (3-year AUC: 0.883; C-index: 0.807). For OS, the optimal multivariate model identified Alb (hazard ratios (HR): 0.226, 95% confidence interval (CI): 0.073-0.697, P = 0.00961), EBV (HR: 3.826, 95% CI: 1.259-11.630, P = 0.017986), SUV
conclusionComparative analysis revealed that BSR outperformed both univariate COX regression and LASSO in selecting variables for NKTCL prognosis modeling. Using BSR, we developed a robust model integrating essential clinical factors and PET/CT-derived radiomic features (e.g., Alb, EBV, SUVmax, LLR and Radscore for OS and age, EBV and Ki67 for PFS), offering a valuable tool for risk stratification and guiding individualized treatment strategies.
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