Department of Nuclear Medicine & PET, All India Institute of Medical Sciences, New Delhi, India; and.
Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York.
J Nucl Med. 2016 Jan;57(1):60-6. doi: 10.2967/jnumed.115.161992. Epub 2015 Oct 8.
The differentiation of idiopathic Parkinson disease (IPD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the most common atypical parkinsonian look-alike syndromes (APS), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve participants with early clinical manifestations. In an initial study, we developed a probabilistic algorithm to classify subjects with clinical parkinsonism but uncertain diagnosis based on the expression of metabolic covariance patterns for IPD, MSA, and PSP. Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered.
We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 y or less at the time of imaging. After imaging, subjects were followed by blinded movement disorders specialists for an average of 2.2 y before final diagnosis was made. When the algorithm was applied to the individual scan data, the probabilities of IPD, MSA, and PSP were computed and used to classify each of the subjects. The resulting image-based classifications were then compared with the final clinical diagnosis.
IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV) using the original 2-level logistic classification algorithm. The algorithm achieved 90% specificity and 85% PPV for MSA and 94% specificity and 94% PPV for PSP. The diagnostic accuracy was similarly high (specificity and PPV > 90%) for parkinsonian subjects with short symptom duration. In addition, 25 subjects were classified as level I indeterminate parkinsonism and 4 more subjects as level II indeterminate APS.
Automated pattern-based image classification can improve the diagnostic accuracy in patients with parkinsonism, even at early disease stages.
特发性帕金森病（IPD）与多系统萎缩症（MSA）和进行性核上性麻痹（PSP）的区分，是最常见的非典型帕金森综合征（APS），临床极具挑战性。在这些疾病中，当体征和症状较轻时，在疾病的早期临床病程中，诊断的准确性较低。在潜在疾病修饰剂的试验中，诊断的不准确性可能尤其重要，这些试验通常涉及有早期临床表现的参与者。在一项初步研究中，我们开发了一种概率算法，根据 IPD、MSA 和 PSP 的代谢协变模式的表达，对具有临床帕金森病但诊断不确定的患者进行分类。基于该算法的分类与最终临床诊断非常吻合。尽管如此，在考虑常规使用该算法之前，需要进行盲法前瞻性验证。
我们使用代谢成像研究了一组 129 名诊断不确定的帕金森病患者的独立队列；其中 77 名（60%）在成像时的症状持续时间不到 2 年。成像后，由盲法运动障碍专家对患者进行平均 2.2 年的随访，然后确定最终诊断。当将算法应用于个体扫描数据时，计算出 IPD、MSA 和 PSP 的概率，并用于对每个患者进行分类。然后将基于图像的分类与最终临床诊断进行比较。
使用原始的 2 级逻辑分类算法，将 IPD 患者与 APS 患者区分开来，特异性为 94%，阳性预测值（PPV）为 96%。该算法对 MSA 的特异性为 90%，PPV 为 85%，对 PSP 的特异性为 94%，PPV 为 94%。对于症状持续时间较短的帕金森病患者，诊断准确性也很高（特异性和 PPV>90%）。此外，25 名患者被归类为 1 级不确定帕金森病，4 名患者被归类为 2 级不确定 APS。
即使在疾病早期阶段，基于自动模式的图像分类也可以提高帕金森病患者的诊断准确性。
