Ararat Chakhoyan1,2, Davis C Woodworth1,2,3, Robert J Harris1,2, Albert Lai4,5, Phioanh L Nghiemphu4,5, Linda M Liau6, Whitney B Pope2, Timothy F Cloughesy4,5, Benjamin M Ellingson7,8,9,10,11. 1. UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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. Department of Biomedical Physics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. 4. UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA. 5. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. 6. Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. 7. UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 8. Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 9. Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 10. UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu. 11. UCLA Brain Tumor Imaging Laboratory (BTIL), Biomedical Physics, Psychiatry, and Bioengineering, 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 quantify changes and prognostic value of diffusion MRI measurements obtained using mono-exponential, diffusion kurtosis imaging (DKI) and stretched exponential (SE) models prior and after chemoradiation in newly diagnosed glioblastoma (GBM). METHODS: Diffusion-weighted images (DWIs) were acquired in twenty-three patients following surgery, prior chemoradiation and within 7 days following completion of treatment, using b-values ranging from 0 to 5000s/mm2. Mono-exponential diffusion (apparent diffusion coefficient: ADC), isotropic (non-directional) DKI model with apparent diffusivity (Dapp) and kurtosis (Kapp) estimates as well as SE model with distributed-diffusion coefficient (DDC) and mean intra-voxel heterogeneity (α) were computed for all patients prior and after chemoradiation. Median values were calculated for normal appearing white matter (NAWM) and contrast-enhancing tumor (CET). The magnitudes of diffusion change prior and after chemoradiation were used to predict overall survival (OS). RESULTS: Diffusivity in NAWM was consistent for all diffusion measures during chemoradiation, while diffusivity measurements (ADC, Dapp and DDC) within CET changed significantly. A strong positive correlation existed between ADC, Dapp, and DDC measurements prior to chemoradiation; however, this association was weak following chemoradiation, suggesting a more complex microstructural environment after cytotoxic therapy. When combined with baseline tumor volume and MGMT status, age and ADC changes added significant prognostic values, whereas more complex diffusion models did not show significant value in predicting OS. CONCLUSIONS: Despite increased tissue complexity following chemoradiation, advanced diffusion models have longer acquisition times, provide largely comparable measures of diffusivity, and do not appear to provide additional prognostic value compared to mono-exponential ADC maps.
PURPOSE: To quantify changes and prognostic value of diffusion MRI measurements obtained using mono-exponential, diffusion kurtosis imaging (DKI) and stretched exponential (SE) models prior and after chemoradiation in newly diagnosed glioblastoma (GBM). METHODS: Diffusion-weighted images (DWIs) were acquired in twenty-three patients following surgery, prior chemoradiation and within 7 days following completion of treatment, using b-values ranging from 0 to 5000s/mm2. Mono-exponential diffusion (apparent diffusion coefficient: ADC), isotropic (non-directional) DKI model with apparent diffusivity (Dapp) and kurtosis (Kapp) estimates as well as SE model with distributed-diffusion coefficient (DDC) and mean intra-voxel heterogeneity (α) were computed for all patients prior and after chemoradiation. Median values were calculated for normal appearing white matter (NAWM) and contrast-enhancing tumor (CET). The magnitudes of diffusion change prior and after chemoradiation were used to predict overall survival (OS). RESULTS: Diffusivity in NAWM was consistent for all diffusion measures during chemoradiation, while diffusivity measurements (ADC, Dapp and DDC) within CET changed significantly. A strong positive correlation existed between ADC, Dapp, and DDC measurements prior to chemoradiation; however, this association was weak following chemoradiation, suggesting a more complex microstructural environment after cytotoxic therapy. When combined with baseline tumor volume and MGMT status, age and ADC changes added significant prognostic values, whereas more complex diffusion models did not show significant value in predicting OS. CONCLUSIONS: Despite increased tissue complexity following chemoradiation, advanced diffusion models have longer acquisition times, provide largely comparable measures of diffusivity, and do not appear to provide additional prognostic value compared to mono-exponential ADC maps.
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