Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Alzheimer Center Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands.
Alzheimers Dement. 2023 Aug;19(8):3261-3271. doi: 10.1002/alz.12939. Epub 2023 Feb 7.
Sporadic Creutzfeldt-Jakob disease (sCJD) comprises multiple subtypes (MM1, MM2, MV1, MV2C, MV2K, VV1, and VV2) with distinct disease durations and spatiotemporal cascades of brain lesions. Our goal was to establish the ante mortem diagnosis of sCJD subtype, based on patient-specific estimates of the spatiotemporal cascade of lesions detected by diffusion-weighted magnetic resonance imaging (DWI).
We included 488 patients with autopsy-confirmed diagnosis of sCJD subtype and 50 patients with exclusion of prion disease. We applied a discriminative event-based model (DEBM) to infer the spatiotemporal cascades of lesions, derived from the DWI scores of 12 brain regions assigned by three neuroradiologists. Based on the DEBM cascades and the prion protein genotype at codon 129, we developed and validated a novel algorithm for the diagnosis of the sCJD subtype.
Cascades of MM1, MM2, MV1, MV2C, and VV1 originated in the parietal cortex and, following subtype-specific orderings of propagation, went toward the striatum, thalamus, and cerebellum; conversely, VV2 and MV2K cascades showed a striatum-to-cortex propagation. The proposed algorithm achieved 76.5% balanced accuracy for the sCJD subtype diagnosis, with low rater dependency (differences in accuracy of ± 1% among neuroradiologists).
Ante mortem diagnosis of sCJD subtype is feasible with this novel data-driven approach, and it may be valuable for patient prognostication, stratification in targeted clinical trials, and future therapeutics.
Subtype diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) is achievable with diffusion MRI. Cascades of diffusion MRI abnormalities in the brain are subtype-specific in sCJD. We proposed a diagnostic algorithm based on cascades of diffusion MRI abnormalities and demonstrated that it is accurate. Our method may aid early diagnosis, prognosis, stratification in clinical trials, and future therapeutics. The present approach is applicable to other neurodegenerative diseases, enhancing the differential diagnoses.
散发性克雅氏病(sCJD)包含多个亚型(MM1、MM2、MV1、MV2C、MV2K、VV1 和 VV2),具有不同的疾病持续时间和时空病变级联。我们的目标是基于扩散加权磁共振成像(DWI)检测到的病变时空级联的患者特异性估计,建立 sCJD 亚型的生前诊断。
我们纳入了 488 例经尸检证实的 sCJD 亚型诊断患者和 50 例排除朊病毒病的患者。我们应用了一种基于事件的判别模型(DEBM)来推断病变的时空级联,该模型源自三位神经放射科医生分配的 12 个脑区的 DWI 评分。基于 DEBM 级联和 129 密码子的朊病毒蛋白基因型,我们开发并验证了一种用于 sCJD 亚型诊断的新算法。
MM1、MM2、MV1、MV2C 和 VV1 的级联始于顶叶皮层,并按照特定的传播顺序向纹状体、丘脑和小脑传播;相反,VV2 和 MV2K 的级联显示出纹状体到皮层的传播。该算法对 sCJD 亚型诊断的平衡准确性达到 76.5%,具有较低的评分者依赖性(神经放射科医生之间的准确性差异为±1%)。
使用这种新的基于数据驱动的方法,sCJD 亚型的生前诊断是可行的,它可能对患者预后、靶向临床试验分层和未来治疗有价值。
使用扩散 MRI 可以实现散发性克雅氏病(sCJD)的亚型诊断。sCJD 大脑中扩散 MRI 异常的级联是亚型特异性的。我们提出了一种基于扩散 MRI 异常级联的诊断算法,并证明其准确性。我们的方法可能有助于早期诊断、预后、临床试验分层和未来治疗。目前的方法适用于其他神经退行性疾病,增强了鉴别诊断。