Hongxi Wang1, Shengnan Zhao2, Li Li1, Rong Tian3. 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. 3. Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China. rongtiannuclear@126.com.
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 (R-signaturePFS: 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.
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 (R-signaturePFS: 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.
Authors: Navid Hasani; Sriram S Paravastu; Faraz Farhadi; Fereshteh Yousefirizi; Michael A Morris; Arman Rahmim; Mark Roschewski; Ronald M Summers; Babak Saboury Journal: PET Clin Date: 2022-01
Authors: Lale Kostakoglu; Federico Dalmasso; Paola Berchialla; Larry A Pierce; Umberto Vitolo; Maurizio Martelli; Laurie H Sehn; Marek Trněný; Tina G Nielsen; Christopher R Bolen; Deniz Sahin; Calvin Lee; Tarec Christoffer El-Galaly; Federico Mattiello; Paul E Kinahan; Stephane Chauvie Journal: EJHaem Date: 2022-03-24
Authors: Yeye Zhou; Jihui Li; Xiaoyi Zhang; Tongtong Jia; Bin Zhang; Na Dai; Shibiao Sang; Shengming Deng Journal: Front Oncol Date: 2022-02-07 Impact factor: 6.244