Department of Neuroradiology, Hannover Medical School, Hannover, Germany.
Swiss Epilepsy Clinic, Klinik Lengg, Zurich, Switzerland.
Mov Disord. 2023 Oct;38(10):1891-1900. doi: 10.1002/mds.29527. Epub 2023 Aug 6.
Brain magnetic resonance imaging (MRI) is used to support the diagnosis of progressive supranuclear palsy (PSP). However, the value of visual descriptive, manual planimetric, automatic volumetric MRI markers and fully automatic categorization is unclear, particularly regarding PSP predominance types other than Richardson's syndrome (RS).
To compare different visual reading strategies and automatic classification of T1-weighted MRI for detection of PSP in a typical clinical cohort including PSP-RS and (non-RS) variant PSP (vPSP) patients.
Forty-one patients (21 RS, 20 vPSP) and 46 healthy controls were included. Three readers using three strategies performed MRI analysis: exclusively visual reading using descriptive signs (hummingbird, morning-glory, Mickey-Mouse), visual reading supported by manual planimetry measures, and visual reading supported by automatic volumetry. Fully automatic classification was performed using a pre-trained support vector machine (SVM) on the results of atlas-based volumetry.
All tested methods achieved higher specificity than sensitivity. Limited sensitivity was driven to large extent by false negative vPSP cases. Support by automatic volumetry resulted in the highest accuracy (75.1% ± 3.5%) among the visual strategies, but performed not better than the midbrain area (75.9%), the best single planimetric measure. Automatic classification by SVM clearly outperformed all other methods (accuracy, 87.4%), representing the only method to provide clinically useful sensitivity also in vPSP (70.0%).
Fully automatic classification of volumetric MRI measures using machine learning methods outperforms visual MRI analysis without and with planimetry or volumetry support, particularly regarding diagnosis of vPSP, suggesting the use in settings with a broad phenotypic PSP spectrum. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
磁共振成像（MRI）用于支持进行进行性核上性麻痹（PSP）的诊断。然而，视觉描述、手动平面测量、自动容积 MRI 标志物和全自动分类的价值尚不清楚，特别是针对除 Richardson 综合征（RS）以外的 PSP 优势类型。
比较不同的视觉阅读策略和自动分类 T1 加权 MRI 对包括 PSP-RS 和（非 RS）变异型 PSP（vPSP）患者的典型临床队列中 PSP 的检测。
共纳入 41 名患者（21 名 RS，20 名 vPSP）和 46 名健康对照者。三名读者使用三种策略进行 MRI 分析：仅使用描述性特征（蜂鸟、牵牛花、米老鼠）的纯视觉阅读、视觉阅读辅以手动平面测量以及视觉阅读辅以自动容积测量。使用基于图谱的容积自动分类使用预训练的支持向量机（SVM）进行。
所有测试方法的特异性均高于敏感性。敏感性有限主要归因于假阴性 vPSP 病例。自动容积测量的支持导致视觉策略中具有最高的准确性（75.1%±3.5%），但并不优于中脑面积（75.9%），即最佳的单个平面测量。SVM 的自动分类明显优于所有其他方法（准确性为 87.4%），代表唯一一种在 vPSP 中也提供有临床价值的敏感性的方法（70.0%）。
使用机器学习方法对容积 MRI 测量值进行全自动分类优于没有和有平面测量或容积测量支持的视觉 MRI 分析，特别是在具有广泛表型 PSP 谱的环境中，建议使用。© 2023 作者。运动障碍由 Wiley Periodicals LLC 代表国际帕金森和运动障碍协会出版。
