Talia C Oughourlian1,2,3, Jingwen Yao1,4, Jacob Schlossman1,2,3, Catalina Raymond1,2, Matthew Ji5,6, Hiroyuki Tatekawa1,2, Noriko Salamon2, Whitney B Pope2, Johannes Czernin7, Phioanh L Nghiemphu5,6, Albert Lai5,6, Timothy F Cloughesy5,6, Benjamin M Ellingson8,9,10,11,12. 1. UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 2. Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 3. Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 4. Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA. 5. UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 6. Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 7. Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 8. UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 9. Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 10. Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 11. Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 12. UCLA Brain Tumor Imaging Laboratory, Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA. bellingson@mednet.ucla.edu.
Abstract
PURPOSE: To examine whether the rate of change in maximum 18F-FDOPA PET uptake and the rate of change in non-enhancing tumor volume could predict malignant transformation and residual overall survival (OS) in low grade glioma (LGG) patients who received serial 18F-FDOPA PET and MRI scans. METHODS: 27 LGG patients with ≥ 2 18F-FDOPA PET and MRI scans between 2003 and 2016 were included. The rate of change in FLAIR volume (uL/day) and maximum normalized 18F-FDOPA specific uptake value (nSUVmax/month), were compared between histological and molecular subtypes. General linear models (GLMs) were used to integrate clinical information with MR-PET measurements to predict malignant transformation. Cox univariate and multivariable regression analyses were performed to identify imaging and clinical risk factors related to OS. RESULTS: A GLM using patient age, treatment, the rate of change in FLAIR and 18F-FDOPA nSUVmax could predict malignant transformation with > 67% sensitivity and specificity (AUC = 0.7556, P = 0.0248). A significant association was observed between OS and continuous rates of change in PET uptake (HR = 1.0212, P = 0.0034). Cox multivariable analysis confirmed that continuous measures of the rate of change in PET uptake was an independent predictor of OS (HR = 1.0242, P = 0.0033); however, stratification of patients based on increasing or decreasing rate of change in FLAIR (HR = 2.220, P = 0.025), PET uptake (HR = 2.148, P = 0.0311), or both FLAIR and PET (HR = 2.354, P = 0.0135) predicted OS. CONCLUSIONS: The change in maximum normalized 18F-FDOPA PET uptake, with or without clinical information and rate of change in tumor volume, may be useful for predicting the risk of malignant transformation and estimating residual survival in patients with LGG.
PURPOSE: To examine whether the rate of change in maximum 18F-FDOPA PET uptake and the rate of change in non-enhancing tumor volume could predict malignant transformation and residual overall survival (OS) in low grade glioma (LGG) patients who received serial 18F-FDOPA PET and MRI scans. METHODS: 27 LGG patients with ≥ 2 18F-FDOPA PET and MRI scans between 2003 and 2016 were included. The rate of change in FLAIR volume (uL/day) and maximum normalized 18F-FDOPA specific uptake value (nSUVmax/month), were compared between histological and molecular subtypes. General linear models (GLMs) were used to integrate clinical information with MR-PET measurements to predict malignant transformation. Cox univariate and multivariable regression analyses were performed to identify imaging and clinical risk factors related to OS. RESULTS: A GLM using patient age, treatment, the rate of change in FLAIR and 18F-FDOPAnSUVmax could predict malignant transformation with > 67% sensitivity and specificity (AUC = 0.7556, P = 0.0248). A significant association was observed between OS and continuous rates of change in PET uptake (HR = 1.0212, P = 0.0034). Cox multivariable analysis confirmed that continuous measures of the rate of change in PET uptake was an independent predictor of OS (HR = 1.0242, P = 0.0033); however, stratification of patients based on increasing or decreasing rate of change in FLAIR (HR = 2.220, P = 0.025), PET uptake (HR = 2.148, P = 0.0311), or both FLAIR and PET (HR = 2.354, P = 0.0135) predicted OS. CONCLUSIONS: The change in maximum normalized 18F-FDOPA PET uptake, with or without clinical information and rate of change in tumor volume, may be useful for predicting the risk of malignant transformation and estimating residual survival in patients with LGG.
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