Lucia Baratto1, Fengyu Wu2, Ryogo Minamimoto3, Negin Hatami1, Tie Liang1, Jean Sabile4, Ranjana H Advani5, Erik Mittra6. 1. Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, Stanford, California, USA. 2. Department of Nuclear Medicine, PET/CT Center, Affiliated Hospital of Qingdao University, Qingdao, China. 3. Division of Nuclear Medicine, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan. 4. Biology Department, University of California, Santa Cruz. 5. Division of Medical Oncology, Department of Medicine, Stanford University, Stanford, California. 6. Division of Nuclear Medicine, Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon, USA.
Abstract
PURPOSE: To determine if the correlation between different metabolic parameters along with clinical features can create an improved model of prognostication for diffuse large B-cell lymphoma (DLBCL) patients. METHODS: We retrospectively evaluated 89 patients with DLBCL. All patients had a baseline and an interim 18F-FDG PET/CT. Seventy-nine also had an end-of-treatment PET/CT (EOT-PET). For each scan, we collected standardized uptake value (SUVmax, SUVmean, SUVpeak), metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmaxsum, SUVmeansum, MTVsum, and TLGsum. These metabolic parameters were combined with clinical features in order to identify a new prognostic model. The predictive value of interim PET and EOT-PET using Deauville score was also determined. RESULTS: Baseline SUVmaxsum and SUVmeansum were significantly correlated to overall survival (OS) (P value = 0.012 and 0.011, respectively). The percentage change of MTV and TLG sum from baseline to EOT was predictive of progression-free survival (PFS) (P value = 0.003 and 0.022, respectively). The combination of either Deauville score at the EOT and SUVmaxsum at baseline significantly predicted OS (P value <0.001); Eastern Cooperative Oncology Group performance status, presence of extranodal disease and percentage change of MTVsum from baseline to EOT were significant predictors of PFS (P value = 0.001). CONCLUSIONS: SUVmaxsum and SUVmeansum at baseline and percentage change in MTV and TLG sum from baseline to EOT are predictors of outcome in DLBCL patients. These metabolic parameters combined to Deauville score and some clinical features could be used together to stratify patients.
PURPOSE: To determine if the correlation between different metabolic parameters along with clinical features can create an improved model of prognostication for diffuse large B-cell lymphoma (DLBCL) patients. METHODS: We retrospectively evaluated 89 patients with DLBCL. All patients had a baseline and an interim 18F-FDG PET/CT. Seventy-nine also had an end-of-treatment PET/CT (EOT-PET). For each scan, we collected standardized uptake value (SUVmax, SUVmean, SUVpeak), metabolic tumor volume (MTV), total lesion glycolysis (TLG), SUVmaxsum, SUVmeansum, MTVsum, and TLGsum. These metabolic parameters were combined with clinical features in order to identify a new prognostic model. The predictive value of interim PET and EOT-PET using Deauville score was also determined. RESULTS: Baseline SUVmaxsum and SUVmeansum were significantly correlated to overall survival (OS) (P value = 0.012 and 0.011, respectively). The percentage change of MTV and TLG sum from baseline to EOT was predictive of progression-free survival (PFS) (P value = 0.003 and 0.022, respectively). The combination of either Deauville score at the EOT and SUVmaxsum at baseline significantly predicted OS (P value <0.001); Eastern Cooperative Oncology Group performance status, presence of extranodal disease and percentage change of MTVsum from baseline to EOT were significant predictors of PFS (P value = 0.001). CONCLUSIONS: SUVmaxsum and SUVmeansum at baseline and percentage change in MTV and TLG sum from baseline to EOT are predictors of outcome in DLBCL patients. These metabolic parameters combined to Deauville score and some clinical features could be used together to stratify patients.
Authors: Christian Philipp Reinert; Regine Mariette Perl; Christoph Faul; Claudia Lengerke; Konstantin Nikolaou; Helmut Dittmann; Wolfgang A Bethge; Marius Horger Journal: J Clin Med Date: 2022-03-10 Impact factor: 4.241