Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom.
Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom; Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
Neuroimage Clin. 2019;24:102051. doi: 10.1016/j.nicl.2019.102051. Epub 2019 Oct 25.
Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by prion protein gene mutations, or exposure to prions in the diet or by medical procedures, such us surgeries. To date, there are no accurate quantitative imaging biomarkers that can be used to predict the future clinical diagnosis of a healthy subject, or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the heterogeneity of phenotypes and the lack of a consistent geometrical pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of human form of prion disease. In this paper, using a tailored framework, we aim to classify and stratify patients with prion disease, according to the severity of their illness. The framework is initialised with the extraction of subject-specific imaging biomarkers. The extracted biomakers are then combined with genetic and demographic information within a Gaussian Process classifier, used to calculate the probability of a subject to be diagnosed with prion disease in the next year. We evaluate the effectiveness of the proposed method in a cohort of patients with inherited and sporadic forms of prion disease. The model has shown to be effective in the prediction of both inherited CJD (92% of accuracy) and sporadic CJD (95% of accuracy). However the model has shown to be less effective when used to stratify the different stages of the disease, in which the average accuracy is 85%, whilst the recall is 59%. Finally, our framework was extended as a differential diagnosis tool to identify both forms of CJD among another neurodegenerative disease. In summary we have developed a novel method for prion disease diagnosis and prediction of clinical onset using multiple sources of features, which may have use in other disorders with heterogeneous imaging features.
朊病毒病是一组罕见的神经退行性疾病，其特征是进展速度快且表型高度异质。虽然最常见的朊病毒病形式是散发性的（散发性克雅氏病，sCJD），但其他形式是由朊病毒蛋白基因突变引起的，或者是由于饮食中接触到朊病毒，或者是由于医疗程序（如手术）引起的。迄今为止，尚无准确的定量成像生物标志物可用于预测健康受试者的未来临床诊断，或量化症状随时间的进展。此外，CJD 常被误诊为其他形式的痴呆症。由于表型的异质性和疾病进展缺乏一致的几何模式，因此用于研究其他类型神经退行性疾病的方法无法令人满意地捕捉人类朊病毒病的进展。在本文中，我们使用定制框架，旨在根据疾病的严重程度对朊病毒病患者进行分类和分层。该框架的初始化是提取与受试者特异性相关的成像生物标志物。然后，将提取的生物标志物与遗传和人口统计学信息结合在高斯过程分类器中，用于计算受试者在未来一年内被诊断患有朊病毒病的概率。我们在遗传性和散发性朊病毒病患者队列中评估了所提出方法的有效性。该模型在遗传性 CJD（准确率为 92%）和散发性 CJD（准确率为 95%）的预测中均表现出有效性。然而，当用于分层疾病的不同阶段时，该模型的效果较差，平均准确率为 85%，而召回率为 59%。最后，我们将该框架扩展为一种鉴别诊断工具，用于在其他神经退行性疾病中识别两种形式的 CJD。总之，我们使用多种来源的特征开发了一种用于朊病毒病诊断和临床发作预测的新方法，该方法可能在具有异质成像特征的其他疾病中具有应用价值。
