Abstract
No disease modifying therapies for Parkinson's disease (PD) have been found effective to date. To properly power clinical trials for discovery of such therapies, the ability to predict outcome in PD is critical, and there is a significant need for discovery of prognostic biomarkers of PD. Dopamine transporter (DAT) SPECT imaging is widely used for diagnostic purposes in PD. In the present work, we aimed to evaluate whether longitudinal DAT SPECT imaging can significantly improve prediction of outcome in PD patients. In particular, we investigated whether radiomics analysis of DAT SPECT images, in addition to use of conventional non-imaging and imaging measures, could be used to predict motor severity at year 4 in PD subjects. We selected 64 PD subjects (38 male, 26 female; age at baseline (year 0): 61.9 ± 7.3, range [46,78]) from the Parkinson's Progressive Marker Initiative (PPMI) database. Inclusion criteria included (i) having had at least 2 SPECT scans at years 0 and 1 acquired on a similar scanner, (ii) having undergone a high-resolution 3 T MRI scan, and (iii) having motor assessment (MDS-UPDRS-III) available in year 4 used as outcome measure. Image analysis included automatic region-of-interest (ROI) extraction on MRI images, registration of SPECT images onto the corresponding MRI images, and extraction of radiomic features. Non-imaging predictors included demographics, disease duration as well as motor and non-motor clinical measures in years 0 and 1. The image predictors included 92 radiomic features extracted from the caudate, putamen, and ventral striatum of DAT SPECT images at years 0 and 1 to quantify heterogeneity and texture in uptake. Random forest (RF) analysis with 5000 trees was used to combine both non-imaging and imaging variables to predict motor outcome (UPDRS-III: 27.3 ± 14.7, range [3,77]). The RF prediction was evaluated using leave-one-out cross-validation. Our results demonstrated that addition of radiomic features to conventional measures significantly improved (p < 0.001) prediction of outcome, reducing the absolute error of predicting MDS-UPDRS-III from 9.00 ± 0.88 to 4.12 ± 0.43. This shows that radiomics analysis of DAT SPECT images has a significant potential towards development of effective prognostic biomarkers in PD.
Highlights
Parkinson's disease (PD) is a progressive, degenerative movement disorder, characterized by neuronal loss in the substantia nigra with the loss of dopaminergic terminals in the basal ganglia (Brooks et al, 1990; Garnett et al, 1987; Stoessl et al, 2011)
A significant way in which dopamine transporter (DAT) SPECT imaging has been helpful is to identify a subgroup of PD patients who are symptomatic without evidence of dopamine deficit (SWEDDs)
We hypothesize that radiomics analysis has the potential to significantly improve prediction of outcome in Parkinson's disease patients
Summary
Parkinson's disease (PD) is a progressive, degenerative movement disorder, characterized by neuronal loss in the substantia nigra with the loss of dopaminergic terminals in the basal ganglia (Brooks et al, 1990; Garnett et al, 1987; Stoessl et al, 2011). There is significant interest in prognostication of disease outcome, to properly adapt and power clinical trial studies, as applied to appropriate patients. Stratification of PD based on expected prognosis would allow better designs of disease modifying trials, with greater power to ascertain efficacy. A significant way in which DAT SPECT imaging has been helpful is to identify a subgroup of PD patients who are symptomatic without evidence of dopamine deficit (SWEDDs). These patients have a significantly better prognosis. What remains of critical importance, is to discover further subsets in the PD population of different outcomes, to enable significantly improved targeted clinical trials for the assessment of novel therapies for PD
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