Palumbo Barbara, Fravolini Mario Luca, Buresta Tommaso, Pompili Filippo, Forini Nevio, Nigro Pasquale, Calabresi Paolo, Tambasco Nicola
From the Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences (BP, TB, NF); Department of Engineering (MLF, FP); Neurology, Perugia University Hospital and Section of Neurology, Department of Medicine, University of Perugia, Perugia (PN, PC, NT); and I.R.C.C.S. Santa Lucia, Rome, Italy (PC).
Medicine (Baltimore). 2014 Dec;93(27):e228. doi: 10.1097/MD.0000000000000228.
Brain single-photon-emission-computerized tomography (SPECT) with I-ioflupane (I-FP-CIT) is useful to diagnose Parkinson disease (PD). To investigate the diagnostic performance of I-FP-CIT 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.I-FP-CIT SPECT with BasGan analysis was 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 study we 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 by CL and CR, while in the third by PL and PR descriptors. Age was added as a further descriptor to evaluate its influence in the classification performance.I-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. I-FP-CIT SPECT with BasGan analysis using SVM classifier was able to diagnose PD. Putamen was the most discriminative descriptor for PD and the patient age influenced the classification accuracy.
使用碘氟潘（I-FP-CIT）的脑单光子发射计算机断层扫描（SPECT）对诊断帕金森病（PD）很有用。为了通过基底神经节V2软件（BasGan）进行半定量分析来研究I-FP-CIT脑SPECT的诊断性能，我们使用支持向量机分类器（SVM，一种强大的监督分类算法）评估了疑似PD患者的半定量数据。对90例表现出轻度症状（运动迟缓-僵硬和轻度震颤）的疑似PD患者进行了I-FP-CIT SPECT与BasGan分析。56例患者确诊为PD，34例为非PD（特发性震颤和药物性帕金森综合征）。至少6个月的临床随访证实了诊断。为了研究BasGan的诊断性能，我们使用“留一法”和“五折法”训练了具有不同描述符的SVM分类模型。在第一项研究中，我们将左（L）、右（R）壳核（P）以及L和R尾状核（C）中的半定量放射性药物摄取值用作类别描述符，总共4个描述符（CL、CR、PL、PR）。在第二项研究中，每个患者仅用CL和CR描述，而在第三项研究中用PL和PR描述符。加入年龄作为进一步的描述符以评估其对分类性能的影响。I-FP-CIT SPECT与BasGan分析在所有模型中的分类性能均高于73.9%。考虑“留一法”，PL和PR比CL和CR描述符是更好的预测指标（所有患者的准确率为91%）；在不同组中，使用PL、PR、CL和CR的诊断准确率与PL和PR描述符相似。加入年龄作为进一步的描述符后，所有模型的准确率均有所提高。在PD和非PD受试者中使用所有5个描述符均获得了最佳结果（CR和CL + PR和PL + 年龄分别为96.4%和94.1%）。“五折法”也观察到了类似结果。使用SVM分类器的I-FP-CIT SPECT与BasGan分析能够诊断PD。壳核是PD最具鉴别力的描述符，患者年龄影响分类准确率。
