Artificial Neural Network–Based Prediction of Outcome in Parkinson’s Disease Patients Using DaTscan SPECT Imaging Features
Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson’s disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques.
We designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson’s Progressive Marker Initiative (PPMI) database. The task was to predict the unified PD rating scale (UPDRS) part III motor score in year 4 from 92 imaging features extracted on 12 different regions as well as 6 non-imaging measures at baseline (year 0). We first performed univariate screening (including the adjustment for false discovery) to select 4 regions each having 10 features with significant performance in classifying year 4 motor outcome into two classes of patients (divided by the UPDRS III threshold of 30). The leave-one-out strategy was then applied to train and test the ANNs for individual and combinations of features. The prediction statistics were calculated from 100 rounds of experiments, and the accuracy in appropriate prediction (classification of year 4 outcome) was quantified.
Out of the baseline non-imaging features, only the UPDRS III (at year 0) was predictive, while multiple imaging features depicted significance. The different selected features reached a predictive accuracy of 70 % if used individually. Combining the top imaging features from the selected regions significantly improved the prediction accuracy to 75 % (p < 0.01). The combination of imaging features with the year 0 UPDRS III score also improved the prediction accuracy to 75 %.
This study demonstrated the added predictive value of radiomic features extracted from DAT SPECT images in serving as a biomarker for PD progression tracking.
Key wordsParkinson’s disease Motor outcome prediction DAT SPECT imaging Artificial neural network
The project was supported by the Michael J. Fox Foundation (Research Grant 2016, ID: 9036.01), including use of data available from the PPMI-a public-private partnership-funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners (listed at www.ppmi-info.org/fundingpartners). This work was also supported by the National Science Foundation (ECCS 1454552), the Natural Sciences and Engineering Research Council of Canada, and the National Natural Science Foundation of China (grant 61628105).
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
- 6.Fahn S, Parkinson Study Group (2005) Does levodopa slow or hasten the rate of progression of Parkinson’s disease? J Neurol 252(Suppl 4):IV37–IV42Google Scholar
- 7.Seibyl J, Jennings D, Tabamo R, Marek K (2005) The role of neuroimaging in the early diagnosis and evaluation of Parkinson’s disease. Minerva Med 96:353–364Google Scholar
- 11.Parkinson Progression Marker Initiative (2011) The Parkinson progression marker initiative (PPMI). Prog Neurobiol 95:629–635Google Scholar
- 16.Caspell-Garcia C, Simuni T, Tosun-Turgut D, Wu IW, Zhang Y, Nalls M, Singleton A, Shaw LA, Kang JH, Trojanowski JQ, Siderowf A, Coffey C, Lasch S, Aarsland D, Burn D, Chahine LM, Espay AJ, Foster ED, Hawkins KA, Litvan I, Richard I, Weintraub D, the Parkinson’s Progression Markers Initiative (PPMI) (2017) Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease. PLoS One 12:e0175674CrossRefGoogle Scholar
- 18.Emrani S, McGuirk A, Xiao W (2017) Prognosis and diagnosis of Parkinson’s disease using multi-task learning. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Halifax, pp 1457–1466Google Scholar
- 19.Brown CJ, Miller SP, Booth BG et al (2015) Prediction of motor function in very preterm infants using connectome features and local synthetic instances. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. pp 69–76Google Scholar
- 21.Guo N, Yen R, El Fakhri G, Li Q (2015) SVM based lung cancer diagnosis using multiple image features in PET/CT. In: 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). pp 1–4Google Scholar
- 25.Koch W, Radau PE, Hamann C, Tatsch K (2005) Clinical testing of an optimized software solution for an automated, observer-independent evaluation of dopamine transporter SPECT studies. J Nucl Med 46:1109–1118Google Scholar
- 27.Evans AC, Collins DL, Mills SR et al (1993) 3D statistical neuroanatomical models from 305 MRI volumes. In: 1993 IEEE conference record nuclear science symposium and medical imaging conference, vol 3. pp 1813–1817Google Scholar
- 29.Friston KJ, Ashburner JT, Kiebel SJ et al (2006) Statistical parametric mapping: The analysis of functional brain images. Elsevier, Burlington, pp 49–62Google Scholar
- 30.Collignon A, Maes F, Delaere D et al (1995) Automated multi-modality image registration based on information theory. In: Information Processing in Medical Imaging. pp 263–274Google Scholar
- 34.Thibault G, Fertil B, Navarro CL et al (2009) Texture indexes and gray level size zone matrix. Application to cell nuclei classification. In: 10th International Conference on Pattern Recognition and Information Processing. pp 140-145Google Scholar
- 35.Zeighami Y, Ulla M, Iturria-Medina Y, Dadar M, Zhang Y, Larcher KMH, Fonov V, Evans AC, Collins DL, Dagher A (2015) Network structure of brain atrophy in de novo Parkinson’s disease. Elife 4: e08440Google Scholar
- 37.Dvornek NC, Ventola P, Pelphrey KA, Duncan JS (2017) Identifying autism from resting-state fMRI using long short-term memory networks. Mach Learn Med Imaging 10541:363-370Google Scholar