Kun-Han Lue1,2, Yi-Feng Wu3, Shu-Hsin Liu1,4, Tsung-Cheng Hsieh5, Keh-Shih Chuang2, Hsin-Hon Lin2,6,7, Yu-Hung Chen1. 1. From the Department of Nuclear Medicine, Buddhist Tzu Chi General Hospital, Hualien. 2. Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu. 3. Department of Hematology and Oncology, Buddhist Tzu Chi General Hospital. 4. Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology. 5. Institute of Medical Sciences, Tzu-Chi University, Hualien. 6. Department of Radiation Oncology, Chang Gung Memorial Hospital. 7. Medical Physics Research Center, Institute for Radiological Research, Chang Gung University/Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Abstract
PURPOSE: This study investigated whether a radiomic analysis of pretreatment F-FDG PET can predict prognosis in patients with Hodgkin lymphoma (HL). METHODS: Forty-two patients who were diagnosed as having HL and underwent pretreatment F-FDG PET scans were retrospectively enrolled. For each patient, we extracted 450 radiomic features from PET images. The prognostic significance of the clinical and radiomic features was assessed in relation to progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic curve, Cox proportional hazards regression, and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate the predictive value. RESULTS: Intensity nonuniformity extracted from a gray-level run-length matrix and the Ann Arbor stage were independently associated with PFS (hazard ratio [HR] = 22.8, P < 0.001; HR = 7.6, P = 0.024) and OS (HR = 14.5, P = 0.012; HR = 8.5, P = 0.048), respectively. In addition, SUV kurtosis was an independent prognosticator for PFS (HR = 6.6, P = 0.026). We devised a prognostic scoring system based on these 3 risk predictors. The proposed scoring system further improved the risk stratification of the current staging classification (P < 0.001). CONCLUSIONS: The radiomic feature intensity nonuniformity is an independent prognostic predictor of PFS and OS in patients with HL. We devised a prognostic scoring system, which may be more beneficial for patient risk stratification in guiding therapy compared with the current Ann Arbor staging system.
PURPOSE: This study investigated whether a radiomic analysis of pretreatment F-FDG PET can predict prognosis in patients with Hodgkin lymphoma (HL). METHODS: Forty-two patients who were diagnosed as having HL and underwent pretreatment F-FDG PET scans were retrospectively enrolled. For each patient, we extracted 450 radiomic features from PET images. The prognostic significance of the clinical and radiomic features was assessed in relation to progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic curve, Cox proportional hazards regression, and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate the predictive value. RESULTS: Intensity nonuniformity extracted from a gray-level run-length matrix and the Ann Arbor stage were independently associated with PFS (hazard ratio [HR] = 22.8, P < 0.001; HR = 7.6, P = 0.024) and OS (HR = 14.5, P = 0.012; HR = 8.5, P = 0.048), respectively. In addition, SUV kurtosis was an independent prognosticator for PFS (HR = 6.6, P = 0.026). We devised a prognostic scoring system based on these 3 risk predictors. The proposed scoring system further improved the risk stratification of the current staging classification (P < 0.001). CONCLUSIONS: The radiomic feature intensity nonuniformity is an independent prognostic predictor of PFS and OS in patients with HL. We devised a prognostic scoring system, which may be more beneficial for patient risk stratification in guiding therapy compared with the current Ann Arbor staging system.
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: Jakoba J Eertink; Gerben J C Zwezerijnen; Matthijs C F Cysouw; Sanne E Wiegers; Elisabeth A G Pfaehler; Pieternella J Lugtenburg; Bronno van der Holt; Otto S Hoekstra; Henrica C W de Vet; Josée M Zijlstra; Ronald Boellaard Journal: Eur J Nucl Med Mol Imaging Date: 2022-08-04 Impact factor: 10.057
Authors: David Morland; Elizabeth Katherine Anna Triumbari; Elena Maiolo; Annarosa Cuccaro; Giorgio Treglia; Stefan Hohaus; Salvatore Annunziata Journal: Front Med (Lausanne) Date: 2022-06-22