Hiroyuki Tatekawa1,2, Akifumi Hagiwara1,2, Hiroyuki Uetani2, Jingwen Yao1,2,3, Talia C Oughourlian1,2,4, Shadfar Bahri5, Chencai Wang1,2, Catalina Raymond1,2, Albert Lai6,7, Timothy F Cloughesy6,7, Phioanh L Nghiemphu6,7, Linda M Liau6,8, Whitney B Pope2, Noriko Salamon2, Benjamin M Ellingson9,10,11,12,13. 1. UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA. 2. Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90024, USA. 3. Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, USA. 4. Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA. 5. Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA. 6. UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA. 7. Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA. 8. Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA. 9. UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA. bellingson@mednet.ucla.edu. 10. Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90024, USA. bellingson@mednet.ucla.edu. 11. Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, USA. bellingson@mednet.ucla.edu. 12. Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA. bellingson@mednet.ucla.edu. 13. UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA. bellingson@mednet.ucla.edu.
Abstract
PURPOSE: To assess whether hypermetabolically-defined regions of interest (ROIs) on 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) could be used to evaluate physiological features and whether there are measurable differences between molecular subtypes and tumor grades. METHODS: Sixty-eight treatment-naïve glioma patients who underwent FDOPA PET and magnetic resonance imaging (MRI) were retrospectively included. Fluid-attenuated inversion recovery hyperintense regions (FLAIRROI) were segmented. FDOPA hypermetabolic regions (FDOPAROI, tumor-to-striatum ratios > 1) within FLAIRROI were extracted. Normalized maximum standardized uptake value (nSUVmax), volume of each ROI, and median relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) within FLAIRROI or FDOPAROI were calculated. Imaging metrics were compared using Students t or Mann-Whitney U tests. Area under the curve (AUC) of receiver-operating characteristic curves were used to determine whether imaging metrics within FLAIRROI or FDOPAROI can discriminate different molecular statuses or grades. RESULTS: Using either FLAIRROI or FDOPAROI, the nSUVmax and rCBV were significantly higher and the ADC was lower in isocitrate dehydrogenase (IDH) wild-type than mutant gliomas, and in higher-grade gliomas (HGGs) than lower-grade gliomas (LGGs). The FDOPAROI volume was significantly higher in 1p19q codeleted than non-codeleted gliomas, and in HGGs than LGGs. Although not significant, imaging metrics extracted by FDOPAROI discriminated molecular status and tumor grade more accurately than those extracted by FLAIRROI (AUC of IDH status, 0.87 vs. 0.82; 1p19q status, 0.78 vs. 0.73; grade, 0.87 vs. 0.76). CONCLUSION: FDOPA hypermetabolic ROI may extract useful imaging features of gliomas, which can illuminate biological differences between different molecular status or tumor grades.
PURPOSE: To assess whether hypermetabolically-defined regions of interest (ROIs) on 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) could be used to evaluate physiological features and whether there are measurable differences between molecular subtypes and tumor grades. METHODS: Sixty-eight treatment-naïve gliomapatients who underwent FDOPA PET and magnetic resonance imaging (MRI) were retrospectively included. Fluid-attenuated inversion recovery hyperintense regions (FLAIRROI) were segmented. FDOPA hypermetabolic regions (FDOPAROI, tumor-to-striatum ratios > 1) within FLAIRROI were extracted. Normalized maximum standardized uptake value (nSUVmax), volume of each ROI, and median relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) within FLAIRROI or FDOPAROI were calculated. Imaging metrics were compared using Students t or Mann-Whitney U tests. Area under the curve (AUC) of receiver-operating characteristic curves were used to determine whether imaging metrics within FLAIRROI or FDOPAROI can discriminate different molecular statuses or grades. RESULTS: Using either FLAIRROI or FDOPAROI, the nSUVmax and rCBV were significantly higher and the ADC was lower in isocitrate dehydrogenase (IDH) wild-type than mutant gliomas, and in higher-grade gliomas (HGGs) than lower-grade gliomas (LGGs). The FDOPAROI volume was significantly higher in 1p19q codeleted than non-codeleted gliomas, and in HGGs than LGGs. Although not significant, imaging metrics extracted by FDOPAROI discriminated molecular status and tumor grade more accurately than those extracted by FLAIRROI (AUC of IDH status, 0.87 vs. 0.82; 1p19q status, 0.78 vs. 0.73; grade, 0.87 vs. 0.76). CONCLUSION:FDOPA hypermetabolic ROI may extract useful imaging features of gliomas, which can illuminate biological differences between different molecular status or tumor grades.
Authors: A Verger; Ph Metellus; Q Sala; C Colin; E Bialecki; D Taieb; O Chinot; D Figarella-Branger; E Guedj Journal: Eur J Nucl Med Mol Imaging Date: 2017-03-14 Impact factor: 9.236
Authors: Bogdana Suchorska; Nathalie L Jansen; Jennifer Linn; Hans Kretzschmar; Hendrik Janssen; Sabina Eigenbrod; Matthias Simon; Gabriele Pöpperl; Friedrich W Kreth; Christian la Fougere; Michael Weller; Joerg C Tonn Journal: Neurology Date: 2015-01-21 Impact factor: 9.910
Authors: Benjamin M Ellingson; Hyun J Kim; Davis C Woodworth; Whitney B Pope; Jonathan N Cloughesy; Robert J Harris; Albert Lai; Phioanh L Nghiemphu; Timothy F Cloughesy Journal: Radiology Date: 2013-11-27 Impact factor: 11.105
Authors: Ian Law; Nathalie L Albert; Javier Arbizu; Ronald Boellaard; Alexander Drzezga; Norbert Galldiks; Christian la Fougère; Karl-Josef Langen; Egesta Lopci; Val Lowe; Jonathan McConathy; Harald H Quick; Bernhard Sattler; David M Schuster; Jörg-Christian Tonn; Michael Weller Journal: Eur J Nucl Med Mol Imaging Date: 2018-12-05 Impact factor: 9.236
Authors: Hiroyuki Tatekawa; Hiroyuki Uetani; Akifumi Hagiwara; Jingwen Yao; Talia C Oughourlian; Issei Ueda; Catalina Raymond; Albert Lai; Timothy F Cloughesy; Phioanh L Nghiemphu; Linda M Liau; Shadfar Bahri; Whitney B Pope; Noriko Salamon; Benjamin M Ellingson Journal: J Neurooncol Date: 2021-03-11 Impact factor: 4.130
Authors: Hiroyuki Tatekawa; Akifumi Hagiwara; Hiroyuki Uetani; Shadfar Bahri; Catalina Raymond; Albert Lai; Timothy F Cloughesy; Phioanh L Nghiemphu; Linda M Liau; Whitney B Pope; Noriko Salamon; Benjamin M Ellingson Journal: Cancer Imaging Date: 2021-03-10 Impact factor: 3.909