Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, Manitoba, Canada.
J Clin Invest. 2022 Oct 17;132(20). doi: 10.1172/JCI157074.
BackgroundCognitive impairment is a common symptom of Parkinson's disease (PD) that increases in risk and severity as the disease progresses. An accurate prediction of the risk of progression from the mild cognitive impairment (MCI) stage to the dementia (PDD) stage is an unmet clinical need.MethodsWe investigated the use of a supervised learning algorithm called the support vector machine (SVM) to retrospectively stratify patients on the basis of brain fluorodeoxyglucose-PET (FDG-PET) scans. Of 43 patients with PD-MCI according to the baseline scan, 23 progressed to PDD within a 5-year period, whereas 20 maintained stable MCI. The baseline scans were used to train a model, which separated patients identified as PDD converters versus those with stable MCI with 95% sensitivity and 91% specificity.ResultsIn an independent validation data set of 19 patients, the AUC was 0.73, with 67% sensitivity and 80% specificity. The SVM model was topographically characterized by hypometabolism in the temporal and parietal lobes and hypermetabolism in the anterior cingulum and putamen and the insular, mesiotemporal, and postcentral gyri. The performance of the SVM model was further tested on 2 additional data sets, which confirmed that the model was also sensitive to later-stage PDD (17 of 19 patients; 89% sensitivity) and dementia with Lewy bodies (DLB) (16 of 17 patients; 94% sensitivity), but not to normal cognition PD (2 of 17 patients). Finally, anti-PD medication status did not change the SVM classification of the other set of 10 patients with PD who were scanned twice, ON and OFF medication.ConclusionsThese results potentially indicate that the proposed FDG-PET-based SVM classifier has utility for providing an accurate prognosis of dementia development in patients with PD-MCI.
背景认知障碍是帕金森病（PD）的常见症状，随着疾病的进展，其风险和严重程度会增加。准确预测从轻度认知障碍（MCI）阶段进展到痴呆（PDD）阶段的风险是一个未满足的临床需求。
方法我们研究了使用称为支持向量机（SVM）的监督学习算法根据脑氟脱氧葡萄糖-PET（FDG-PET）扫描对患者进行回顾性分层。根据基线扫描，43 名 PD-MCI 患者中有 23 名在 5 年内进展为 PDD，而 20 名患者保持稳定的 MCI。基线扫描用于训练模型，该模型以 95%的敏感性和 91%的特异性将被诊断为 PDD 转化者与稳定 MCI 的患者区分开来。
结果在 19 名患者的独立验证数据集中，AUC 为 0.73，敏感性为 67%，特异性为 80%。SVM 模型在颞叶和顶叶代谢低下，前扣带和豆状核以及岛叶、内侧颞叶和中央后回代谢亢进的情况下具有明显的特征。该 SVM 模型的性能在另外 2 个数据集上进行了进一步测试，结果证实该模型对晚期 PDD（19 名患者中的 17 名；89%的敏感性）和路易体痴呆（DLB）（17 名患者中的 16 名；94%的敏感性）也很敏感，但对正常认知 PD（17 名患者中的 2 名）不敏感。最后，抗 PD 药物状态并没有改变对 10 名 PD 患者的另一组扫描的 SVM 分类，这些患者的药物状态 ON 和 OFF 两次。
结论这些结果可能表明，所提出的基于 FDG-PET 的 SVM 分类器可用于为 PD-MCI 患者的痴呆发展提供准确的预后。
