Diagnostic accuracy of parkinson disease by support vector machine (SVM) analysis of 123I-FP-CIT brain SPECT data

Abstract

Brain single-photon-emission-computerized tomography (SPECT) with 123I-ioflupane (123I-FP-CIT) is useful to diagnose Parkinson disease (PD). To investigate the diagnostic performance of 123I-FPCIT brain SPECT with semiquantitative analysis by Basal Ganglia V2 software (BasGan), we evaluated semiquantitative data of patients with suspect of PD by a support vector machine classifier (SVM), a powerful supervised classification algorithm. 123I-FP-CIT SPECT withBasGan analysiswas performed in 90 patients with suspect of PD showing mild symptoms (bradykinesia-rigidity and mild tremor). PD was confirmed in 56 patients, 34 resulted non-PD (essential tremor and drug-induced Parkinsonism). A clinical follow-up of at least 6 months confirmed diagnosis. To investigate BasGan diagnostic performance we trained SVM classification models featuring different descriptors using both a "leave-one-out" and a "five-fold"method. In the first studywe used as class descriptors the semiquantitative radiopharmaceutical uptake values in the left (L) and right (R) putamen (P) and in the L and R caudate nucleus (C) for a total of 4 descriptors (CL, CR, PL, PR). In the second study each patient was described only byCLand CR, while in the third byPLandPRdescriptors. Age was added as a further descriptor to evaluate its influence in the classification performance. 123I-FP-CIT SPECT with BasGan analysis reached a classification performance higher than 73.9% in all the models. Considering the "Leave-one-out" method, PL and PR were better predictors (accuracy of 91%for all patients) than CL and CR descriptors; using PL, PR, CL, and CR diagnostic accuracy was similar to that of PL and PR descriptors in the different groups. Adding age as a further descriptor accuracy improved in all the models. The best results were obtained by using all the 5 descriptors both in PD and non-PD subjects (CR and CL+PR and PL+age=96.4% and 94.1%, respectively). Similar results were observed for the "five-fold" method. 123I-FP-CIT SPECTwith BasGan analysis using SVMclassifierwas able to diagnose PD. Putamen was the most discriminative descriptor for PD and the patient age influenced the classification accuracy.