Robert V Mulkern1, Kelsey I Ricci2, Sridhar Vajapeyam1, Thomas L Chenevert3, Dariya I Malyarenko4, Mehmet Kocak5, Tina Young Poussaint6. 1. Department of Radiology, Harvard Medical School, Boston, Massachusetts; Department of Radiology, Boston Children's Hospital, Boston, Massachusetts. 2. Department of Radiology, Boston Children's Hospital, Boston, Massachusetts. 3. Department of Radiology, University of Michigan Health System, Ann Arbor, Michigan; Department of Radiology, University of Michigan Medical Center, Ann Arbor, Michigan. 4. Department of Radiology, University of Michigan Medical Center, Ann Arbor, Michigan. 5. Division of Biostatistics and Epidemiology, University of Tennessee Health Science Center, Memphis, Tennessee; Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee; Pediatric Brain Tumor Consortium, St Jude Children's Research Hospital, Memphis, Tennessee. 6. Department of Radiology, Harvard Medical School, Boston, Massachusetts; Pediatric Brain Tumor Consortium Neuroimaging Center, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115. Electronic address: tinayoung.poussaint@childrens.harvard.edu.
Abstract
RATIONALE AND OBJECTIVES: Magnetic resonance diffusion imaging can characterize physiologic characteristics of pediatric brain tumors used to assess therapy response. The purpose of this study was to assess the variability of the apparent diffusion coefficient (ADC) along z-axis of scanners in the multicenter Pediatric Brain Tumor Consortium (PBTC). MATERIALS AND METHODS: Ice-water diffusion phantoms for each PBTC site were distributed with a specific diffusion imaging protocol. The phantom was scanned four successive times to 1) confirm water in the tube reached thermal equilibrium and 2) allow for assessment of intra-examination ADC repeatability. ADC profiles across slice positions for each vendor and institution combination were characterized using linear regression modeling with a quadratic fit. RESULTS: Eleven sites collected data with a high degree of compliance to the diffusion protocol for each scanner. The mean ADC value at slice position zero for vendor A was 1.123 × 10(-3) mm(2)/s, vendor B was 1.0964 × 10(-3) mm(2)/s, and vendor C was 1.110 × 10(-3) mm(2)/s. The percentage coefficient of variation across all sites was 0.309% (standard deviation = 0.322). The ADC values conformed well to a second-order polynomial along the z-axis, (ie, following a linear model pattern with quadratic fit) for vendor-institution combinations and across vendor-institution combinations as shown in the longitudinal model. CONCLUSIONS: Assessment of the variability of diffusion metrics is essential for establishing the validity of using these quantitative metrics in multicenter trials. The low variability in ADC values across vendors and institutions and validates the use of ADC as a quantitative tumor marker in pediatric multicenter trials.
RATIONALE AND OBJECTIVES: Magnetic resonance diffusion imaging can characterize physiologic characteristics of pediatric brain tumors used to assess therapy response. The purpose of this study was to assess the variability of the apparent diffusion coefficient (ADC) along z-axis of scanners in the multicenter Pediatric Brain Tumor Consortium (PBTC). MATERIALS AND METHODS: Ice-water diffusion phantoms for each PBTC site were distributed with a specific diffusion imaging protocol. The phantom was scanned four successive times to 1) confirm water in the tube reached thermal equilibrium and 2) allow for assessment of intra-examination ADC repeatability. ADC profiles across slice positions for each vendor and institution combination were characterized using linear regression modeling with a quadratic fit. RESULTS: Eleven sites collected data with a high degree of compliance to the diffusion protocol for each scanner. The mean ADC value at slice position zero for vendor A was 1.123 × 10(-3) mm(2)/s, vendor B was 1.0964 × 10(-3) mm(2)/s, and vendor C was 1.110 × 10(-3) mm(2)/s. The percentage coefficient of variation across all sites was 0.309% (standard deviation = 0.322). The ADC values conformed well to a second-order polynomial along the z-axis, (ie, following a linear model pattern with quadratic fit) for vendor-institution combinations and across vendor-institution combinations as shown in the longitudinal model. CONCLUSIONS: Assessment of the variability of diffusion metrics is essential for establishing the validity of using these quantitative metrics in multicenter trials. The low variability in ADC values across vendors and institutions and validates the use of ADC as a quantitative tumor marker in pediatric multicenter trials.
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