Development and validation of an 18 F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extran
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ONCOLOGY
Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma Hongxi Wang 1 & Shengnan Zhao 2 & Li Li 1 & Rong Tian 1 Received: 14 January 2020 / Revised: 2 April 2020 / Accepted: 7 May 2020 # European Society of Radiology 2020
Abstract Objectives To identify an 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) radiomics-based model for predicting progression-free survival (PFS) and overall survival (OS) of nasal-type extranodal natural killer/T cell lymphoma (ENKTL). Methods In this retrospective study, a total of 110 ENKTL patients were divided into a training cohort (n = 82) and a validation cohort (n = 28). Forty-one features were extracted from pretreatment PET images of the patients. Least absolute shrinkage and selection operator (LASSO) regression was used to develop the radiomic signatures (R-signatures). A radiomics-based model was built and validated in the two cohorts and compared with a metabolism-based model. Results The R-signatures were constructed with moderate predictive ability in the training and validation cohorts (RsignaturePFS: AUC = 0.788 and 0.473; R-signatureOS: AUC = 0.637 and 0.730). For PFS, the radiomics-based model showed better discrimination than the metabolism-based model in the training cohort (C-index = 0.811 vs. 0.751) but poorer discrimination in the validation cohort (C-index = 0.588 vs. 0.693). The calibration of the radiomics-based model was poorer than that of the metabolism-based model (training cohort: p = 0.415 vs. 0.428, validation cohort: p = 0.228 vs. 0.652). For OS, the performance of the radiomics-based model was poorer (training cohort: C-index = 0.818 vs. 0.828, p = 0.853 vs. 0.885; validation cohort: C-index = 0.628 vs. 0.753, p < 0.05 vs. 0.913). Conclusions Radiomic features derived from PET images can predict the outcomes of patients with ENKTL, but the performance of the radiomics-based model was inferior to that of the metabolism-based model. Key Points • The R-signatures calculated by using 18F-FDG PET radiomic features can predict the survival of patients with ENKTL. • The radiomics-based models integrating the R-signatures and clinical factors achieved good predictive values. • The performance of the radiomics-based model was inferior to that of the metabolism-based model in the two cohorts. Keywords Lymphoma . Positron emission tomography . Prognosis
Abbreviations Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06943-1) contains supplementary material, which is available to authorized users.
AUC CI C-index DICOM ECOG PS
* Rong Tian [email protected] 1
Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
2
Department of Oncology, West China Hospital, Sichuan University, No. 37, Lane Guoxue Wuhou District, Chengdu City, Sichuan Province, China
ICC IPI LASSO LDH MTV
Area under curves Confidence interval The Harrell concordan
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