Aatif Parvez1, Noam Tau1, Douglas Hussey1, Manjula Maganti2, Ur Metser3. 1. Joint Department of Medical Imaging, Princess Margaret Cancer Centre, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, 610 University Ave, Suite 3-960, Toronto, ON, M5G 2M9, Canada. 2. Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Canada. 3. Joint Department of Medical Imaging, Princess Margaret Cancer Centre, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, 610 University Ave, Suite 3-960, Toronto, ON, M5G 2M9, Canada. Ur.metser@uhn.ca.
Abstract
PURPOSE: To determine whether metabolic tumor parameters and radiomic features extracted from 18F-FDG PET/CT (PET) can predict response to therapy and outcome in patients with aggressive B-cell lymphoma. METHODS: This institutional ethics board-approved retrospective study included 82 patients undergoing PET for aggressive B-cell lymphoma staging. Whole-body metabolic tumor volume (MTV) using various thresholds and tumor radiomic features were assessed on representative tumor sites. The extracted features were correlated with treatment response, disease-free survival (DFS) and overall survival (OS). RESULTS: At the end of therapy, 66 patients (80.5%) had shown complete response to therapy. The parameters correlating with response to therapy were bulky disease > 6 cm at baseline (p = 0.026), absence of a residual mass > 1.5 cm at the end of therapy CT (p = 0.028) and whole-body MTV with best performance using an SUV threshold of 3 and 6 (p = 0.015 and 0.009, respectively). None of the tumor texture features were predictive of first-line therapy response, while a few of them including GLNU correlated with disease-free survival (p = 0.013) and kurtosis correlated with overall survival (p = 0.035). CONCLUSIONS: Whole-body MTV correlates with response to therapy in patient with aggressive B-cell lymphoma. Tumor texture features could not predict therapy response, although several features correlated with the presence of a residual mass at the end of therapy CT and others correlated with disease-free and overall survival. These parameters should be prospectively validated in a larger cohort to confirm clinical prognostication.
PURPOSE: To determine whether metabolic tumor parameters and radiomic features extracted from 18F-FDG PET/CT (PET) can predict response to therapy and outcome in patients with aggressive B-cell lymphoma. METHODS: This institutional ethics board-approved retrospective study included 82 patients undergoing PET for aggressive B-cell lymphoma staging. Whole-body metabolic tumor volume (MTV) using various thresholds and tumor radiomic features were assessed on representative tumor sites. The extracted features were correlated with treatment response, disease-free survival (DFS) and overall survival (OS). RESULTS: At the end of therapy, 66 patients (80.5%) had shown complete response to therapy. The parameters correlating with response to therapy were bulky disease > 6 cm at baseline (p = 0.026), absence of a residual mass > 1.5 cm at the end of therapy CT (p = 0.028) and whole-body MTV with best performance using an SUV threshold of 3 and 6 (p = 0.015 and 0.009, respectively). None of the tumor texture features were predictive of first-line therapy response, while a few of them including GLNU correlated with disease-free survival (p = 0.013) and kurtosis correlated with overall survival (p = 0.035). CONCLUSIONS: Whole-body MTV correlates with response to therapy in patient with aggressive B-cell lymphoma. Tumor texture features could not predict therapy response, although several features correlated with the presence of a residual mass at the end of therapy CT and others correlated with disease-free and overall survival. These parameters should be prospectively validated in a larger cohort to confirm clinical prognostication.
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: Adam A Dmytriw; Claudia Ortega; Reut Anconina; Ur Metser; Zhihui A Liu; Zijin Liu; Xuan Li; Thiparom Sananmuang; Eugene Yu; Sayali Joshi; John Waldron; Shao Hui Huang; Scott Bratman; Andrew Hope; Patrick Veit-Haibach Journal: Cancers (Basel) Date: 2022-06-24 Impact factor: 6.575