OBJECTIVE: To verify if (123)I-FP-CIT, DaTSCAN(®) can differentiate early stages of Parkinson's disease (PD) as well as patients with Atypical Parkinsonian syndromes (APS) from manifest Parkinson's disease. METHODS: 128 consecutive patients were investigated with (123)I-FP-CIT SPECT during a 4-year period. All patients were diagnosed according to the established consensus criteria for diagnosis of PD (n = 53) and APS (n = 19). Remaining patients were grouped early PD (before onset of L-DOPA medication), (n = 20), vascular PD (n = 6), and non-PD syndromes (n = 30) and SWEDD (n = 1). SPECT images were analyzed visually according to a predefined ranking scale of dopaminergic nerve cell degeneration, distinguishing a posterior-anterior degeneration pattern (egg shape) from a more global and severe degeneration pattern (burst striatum). Striatum uptake ratios were quantitatively analyzed with the 3D software, EXINI. RESULTS: In the group of APS patients, the burst striatum pattern was most frequent and found in 61 % (11/18 patients). In PD patients, the egg shape pattern was dominating, especially in early PD where it was present in 95 % (19/20 patients). The positive predictive value for the egg shape pattern to diagnose PD was 92 % in this material (APS and all PD patients) and the specificity 90 % for the burst striatum pattern to exclude APS. The uptake ratios were reduced in both PD and APS patients and closely related to the image ranking. CONCLUSION: In this study, we found that in more than half of the patients it was possible to differentiate between PD and APS by visual interpretation only. Similar results were obtained using semi-quantitative uptake ratios. Combining visual assessment with uptake ratios did not add to the discriminating power of DaTSCAN(®) SPECT in this material.
OBJECTIVE: To verify if (123)I-FP-CIT, DaTSCAN(®) can differentiate early stages of Parkinson's disease (PD) as well as patients with Atypical Parkinsonian syndromes (APS) from manifest Parkinson's disease. METHODS: 128 consecutive patients were investigated with (123)I-FP-CIT SPECT during a 4-year period. All patients were diagnosed according to the established consensus criteria for diagnosis of PD (n = 53) and APS (n = 19). Remaining patients were grouped early PD (before onset of L-DOPA medication), (n = 20), vascular PD (n = 6), and non-PD syndromes (n = 30) and SWEDD (n = 1). SPECT images were analyzed visually according to a predefined ranking scale of dopaminergic nerve cell degeneration, distinguishing a posterior-anterior degeneration pattern (egg shape) from a more global and severe degeneration pattern (burst striatum). Striatum uptake ratios were quantitatively analyzed with the 3D software, EXINI. RESULTS: In the group of APSpatients, the burst striatum pattern was most frequent and found in 61 % (11/18 patients). In PDpatients, the egg shape pattern was dominating, especially in early PD where it was present in 95 % (19/20 patients). The positive predictive value for the egg shape pattern to diagnose PD was 92 % in this material (APS and all PDpatients) and the specificity 90 % for the burst striatum pattern to exclude APS. The uptake ratios were reduced in both PD and APSpatients and closely related to the image ranking. CONCLUSION: In this study, we found that in more than half of the patients it was possible to differentiate between PD and APS by visual interpretation only. Similar results were obtained using semi-quantitative uptake ratios. Combining visual assessment with uptake ratios did not add to the discriminating power of DaTSCAN(®) SPECT in this material.
Authors: Nicolas Nicastro; Valentina Garibotto; Antoine Poncet; Simon Badoud; Pierre R Burkhard Journal: Mol Imaging Biol Date: 2016-04 Impact factor: 3.488
Authors: Jim J Lloyd; George Petrides; Paul C Donaghy; Sean J Colloby; Johannes Attems; John T O'Brien; Gemma Roberts; Alan J Thomas Journal: Neuroimage Clin Date: 2018-09-20 Impact factor: 4.881
Authors: Enrico Premi; V D Calhoun; V Garibotto; R Turrone; A Alberici; E Cottini; A Pilotto; S Gazzina; M Magoni; B Paghera; B Borroni; A Padovani Journal: Mol Imaging Biol Date: 2017-10 Impact factor: 3.488