Development and Validation of Clinical-Metabolic-Radiomics Model Based on Nomogram-Revised Risk Index for Prognosis Prediction in Patients with Extranodal Natural Kill/T Cell Lymphoma

Abstract

To identify clinical-metabolic-radiomics model using clinical data and 18F-FDG PET/CT image for predicting progression-free survival (PFS) of nasal-type extranodal natural killer/T cell lymphoma (ENKTCL) on the basis of the nomogram-revised risk index (NRI) model previously established and validated by our research group. A total of 133 ENKTCL patients were prospectively included and randomly divided into a training cohort (n = 73) and a validation cohort (n = 50). 107 features and 7 commonly used metabolic parameters (SUVmax, MTV, TLG, SD, TLR, TAR and TBR) were extracted from baseline PET images of the patients. Least absolute shrinkage and selection operator (LASSO) following Cox regression were used to select optimal features and parameters. NRI-metabolic-radiomics model was developed and validated in the two cohorts and compared with NRI model and NRI-metabolic model. TLG and 5 radiomics features were selected after LASSO and Cox regression. NRI-metabolic (NRI-TLG) model and NRI-metabolic-radiomics (NRI-TLG-RAD) model was developed based on NRI, TLG and selected 5 radiomics features. For PFS, NRI-TLG-RAD showed better PFS discrimination than NRI-TLG model and NRI model in both training cohort (C-index = 0.791, 0.743 and 0.690, respectively) and validation cohort (C-index = 0.785, 0.707, and 0.610 respectively). Moreover, NRI-TLG-RAD model and NRI-TLG model divided more patients into low-risk group (No. of patients: 66, 42 vs. 20) and very high-risk group (No. of patients: 25, 25 vs. 9), compared to preexisting NRI model. The addition of metabolic and radiomics information improved the prognostic performance of preexisting NRI model greatly. Better prognostic discrimination and more reasonable patient division of the new NRI-TLG and NRI-TLG-RAD model may provide the basis for more precise treatment modality in the future.