Affiliations: [a]
Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| [b] Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| [c]
Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| [d]
Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Correspondence:
[*]
Correspondence to: Prof. Minming Zhang, M.D, Ph.D, Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China. Tel.: +86 571 88981063; Fax: +86 571 87315255; E-mail: [email protected].
Correspondence:
[*]
Correspondence to: Prof. Shunren Xia, Ph.D, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310013, China. Tel.: +86 571 88285706; Fax: +86 571 87951676; E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: Background: Patients with the postural instability and gait difficulty subtype (PIGD) of Parkinson’s disease (PD) are a refractory challenge in clinical practice. Despite previous attempts that have been made at studying subtype-specific brain alterations across PD population, conclusive neuroimaging biomarkers on patients with the PIGD subtype are still lacking. Machine learning-based classifications are a promising tool for differential diagnosis that effectively integrate complex and multivariate data. Objective: Our present study aimed to introduce the machine learning-based automatic classification for the first time to distinguish patients with the PIGD subtype from those with the non-PIGD subtype of PD at the individual level. Methods: Fifty-two PD patients and forty-five normal controls (NCs) were recruited and underwent multi-modal MRI scans including a set of resting-state functional, 3D T1-weighted and diffusion tensor imaging sequences. By comparing the PD patients with the NCs, features that were not conducive to the subtype-specific classification were ruled out from massive brain features. We applied a support vector machine classifier with the recursive feature elimination method to multi-modal MRI data for selecting features with the best discriminating power, and evaluated the proposed classifier with the leave-one-out cross-validation. Results: Using this classifier, we obtained satisfactory diagnostic rates (accuracy = 92.31%, specificity = 96.97%, sensitivity = 84.21% and AUCmax = 0.9585). The diagnostic agreement evaluated by the Kappa test showed an almost perfect agreement with the existing clinical categorization (Kappa value = 0.83). Conclusions: With these favorable results, our findings suggested the machine learning-based classification as an alternative technique to classifying clinical subtypes in PD.
