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Semiquantitative Analysis of Dopamine Transporter Scans in Patients With Parkinson Disease

Tinaz, Sule, MD, PhD*; Chow, Christopher, BS*; Kuo, Phillip, H., MD, PhD; Krupinski, Elizabeth, A., PhD; Blumenfeld, Hal, MD, PhD*; Louis, Elan, D., MD, MS*§∥; Zubal, George, PhD

doi: 10.1097/RLU.0000000000001885
Original Articles

Purpose Dopamine transporter (DaT) imaging is an adjunct diagnostic tool in parkinsonian disorders. Interpretation of DaT scans is based on visual reads. SBRquant is an automated method that measures the striatal binding ratio (SBR) in DaT scans, but has yet to be optimized. We aimed to (1) optimize SBRquant parameters to distinguish between patients with Parkinson disease (PD) and healthy controls using the Parkinson's Progression Markers Initiative (PPMI) database and (2) test the validity of these parameters in an outpatient cohort.

Methods For optimization, 336 DaT scans (215 PD patients and 121 healthy controls) from the PPMI database were used. Striatal binding ratio was calculated varying the number of summed transverse slices (N) and positions of the striatal regions of interest (d). The resulting SBRs were evaluated using area under the receiver operating characteristic curve. The optimized parameters were then applied to 77 test patients (35 PD and 42 non-PD patients). Striatal binding ratios were also correlated with clinical measures in the PPMI-PD group.

Results The optimal parameters discriminated the training groups in the PPMI cohort with 95.8% sensitivity and 98.3% specificity (lowest putamen SBR threshold, 1.037). The same parameters discriminated the groups in the test cohort with 97.1% sensitivity and 100% specificity (lowest putamen SBR threshold, 0.875). A significant negative correlation (r = −0.24, P = 0.0004) was found between putamen SBRs and motor severity in the PPMI-PD group.

Conclusions SBRquant discriminates DaT scans with high sensitivity and specificity. It has a high potential for use as a quantitative diagnostic aid in clinical and research settings.

From the *Department of Neurology, Yale School of Medicine, New Haven, CT; †Departments of Medical Imaging, Medicine and Biomedical Engineering, University of Arizona Health Sciences, Tucson, AZ; ‡Department of Radiology & Imaging Sciences, Emory University, Atlanta, GA; §Department of Chronic Disease Epidemiology, Yale School of Public Health; ∥Center for Neuroepidemiology and Clinical Neurological Research, Yale School of Medicine; and ¶Z-Concepts LLC, New Haven, CT.

Received for publication June 4, 2017; revision accepted October 1, 2017.

Conflicts of interest and sources of funding: P.H.K. has received support as a consultant, investigator, and/or speaker for Genentech, General Electric Healthcare, inviCRO, Lilly, Merck, MD Training @Home, Molecular Neuroimaging Institute, and Navidea. G.Z. has received support from the National Institutes of Health (NIH): STTR phase II renewal (5R42NS055475-06, principal investigator), SBIR FastTrack (NS055475, principal investigator), and the Department of Energy (DOE): SBIR phase I (83229S07-I, principal investigator). The other authors have none declared. This work was supported by the following grants: NIH STTR phase II renewal (5R42NS055475), NIH SBIR FastTrack (NS055475), and DOE SBIR phase I (83229S07-I).

Data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative database ( For up-to-date information on the study, visit The Parkinson's Progression Markers Initiative, a public-private partnership, is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including Abbvie, Avid, Biogen, Bristol-Myers Squibb, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Roche, Sanfo Genzyme, Servier, Takeda, Teva, UCB, and Golub Capital.

Correspondence to: Sule Tinaz, MD, PhD, 15 York St, LCI 710, New Haven, CT 06510. E-mail:

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