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A clinical-radiomic model based on best subset regression for prognostic prediction in natural killer/T-cell lymphoma
Fangfang Tian1, Longlan Chen2, Xiaoliang Chen3
1Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
Background:
The 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.
Methods:
This 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 (SUVmax), lumbar-to-liver SUVmean ratio (LLR), and spleen-to-liver SUVmean ratio (SLR)). Variable selection was performed using univariate COX (P < 0.1), LASSO, and BSR techniques. The resulting models were compared using the area under the curve (AUC) and concordance index (C-index), with internal validation via bootstrapping.
Results:
The 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), SUVmax (HR: 2.466, 95% CI: 1.123-5.416, P = 0.024517), LLR (HR: 0.195, 95% CI: 0.077-0.493, P = 0.000549), and Radscore (HR: 15.463, 95% CI: 5.313-45.001, P = 5.05e-07) as independent prognostic factors. For PFS, the optimal model comprised age (HR: 2.292, 95% CI: 1.076-4.884, P = 0.03166), EBV (HR: 4.168, 95% CI: 1.588-10.936, P = 0.00373), and Ki67 (HR: 2.608, 95% CI: 1.221-5.572, P = 0.01334).
Conclusion:
Comparative 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.
