Saito Yuya, Kamagata Koji, Wijeratne Peter A, Andica Christina, Uchida Wataru, Takabayashi Kaito, Fujita Shohei, Akashi Toshiaki, Wada Akihiko, Shimoji Keigo, Hori Masaaki, Masutani Yoshitaka, Alexander Daniel C, Aoki Shigeki
Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Front Neurol. 2022 Feb 25;13:814768. doi: 10.3389/fneur.2022.814768. eCollection 2022.
Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)-a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS.
将表现为皮质基底节综合征（CBD-CBS）的皮质基底节变性与伴有理查森综合征的进行性核上性麻痹（PSP-RS）区分开来，尤其是在疾病早期，往往具有挑战性，因为这两种神经退行性疾病在临床表现和病理学方面密切重叠。尽管已经对CBS和PSP-RS患者进行了使用脑磁共振成像（MRI）的体积测量研究，但评估脑萎缩进展的研究仍然有限。因此，我们旨在单纯基于横断面数据，使用亚型和阶段推断（SuStaIn）——一种整合聚类和疾病进展建模的新型无监督机器学习技术，揭示CBS患者和PSP-RS患者脑萎缩的时间进展模式差异。我们将SuStaIn应用于25例CBS患者、39例典型PSP-RS患者和50名健康对照的横断面脑区体积，以估计CBS和PSP-RS这两种具有不同萎缩模式的疾病亚型和病程。将CBS和PSP-RS的进展模型及分类准确性与先前研究进行比较，以评估SuStaIn的性能。SuStaIn识别出了CBS和PSP-RS不同的脑萎缩时间进展模式，这在很大程度上与先前的证据一致，在交叉验证下具有较高的可重复性（99.7%）。基于横断面结构脑MRI数据，我们对这些疾病进行了高精度（0.875）的分类，敏感性分别为0.680和1.000；该准确性高于先前研究报道。此外，SuStaIn阶段在没有疾病阶段标签（如病程）的情况下正确反映了疾病严重程度。此外，SuStaIn还显示出对CBS和PSP-RS的分化和反映的泛化性能。因此，SuStaIn在增进我们对疾病机制的理解、准确对患者进行分层以及为CBS和PSP-RS患者提供预后方面具有潜力。
