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Multisite Concordance of Apparent Diffusion Coefficient Measurements across the NCI Quantitative Imaging Network

University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States.
Publication Date:
J Med Imaging (Bellingham)
Volume Number:
Issue Number:
J Med Imaging (Bellingham). 2018 Jan;5(1):011003.
PubMed ID:
Appears in Collections:
NCIGT, Prostate Group, SPL
P41 EB015898/EB/NIBIB NIH HHS/United States
U24 CA180918/CA/NCI NIH HHS/United States
R50 CA211270/CA/NCI NIH HHS/United States
U01 CA148131/CA/NCI NIH HHS/United States
U01 CA151235/CA/NCI NIH HHS/United States
U01 CA154601/CA/NCI NIH HHS/United States
U24 CA180927/CA/NCI NIH HHS/United States
U01 CA176110/CA/NCI NIH HHS/United States
Generated Citation:
Newitt D.C., Malyarenko D., Chenevert T.L., Quarles C.C., Bell L., Fedorov A., Fennessy F., Jacobs M.A., Solaiyappan M., Hectors S., Taouli B., Muzi M., Kinahan P.E., Schmainda K.M., Prah M.A., Taber E.N., Kroenke C., Huang W., Arlinghaus L.R., Yankeelov T.E., Cao Y., Aryal M., Yen Y-F., Kalpathy-Cramer J., Shukla-Dave A., Fung M., Liang J., Boss M., Hylton N. Multisite Concordance of Apparent Diffusion Coefficient Measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham). 2018 Jan;5(1):011003. PMID: 29021993. PMCID: PMC5633866.
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Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.