Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Centre for Medical Image Computing (CMIC), Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK.
Nat Commun. 2021 Apr 6;12(1):2078. doi: 10.1038/s41467-021-22265-2.
Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.
多发性硬化症 (MS) 可根据临床演变分为四种表型。这些表型的病理生理界限尚不清楚，限制了治疗分层。机器学习可以使用多维数据识别具有相似特征的组。在这里，为了根据病理特征对 MS 亚型进行分类，我们将无监督机器学习应用于先前发表的研究中获得的脑 MRI 扫描。我们使用来自 6322 名 MS 患者的训练数据集来定义基于 MRI 的亚型，并使用 3068 名患者的独立队列进行验证。基于最早的异常，我们将 MS 亚型定义为皮质主导型、正常表现的白质主导型和病变主导型。病变主导型患者发生确诊残疾进展 (CDP) 的风险最高，复发率也最高。在选定的临床试验中，病变主导型 MS 患者显示出积极的治疗反应。我们的研究结果表明，基于 MRI 的亚型可预测 MS 残疾进展和对治疗的反应，并且可能用于在干预性试验中定义患者群体。
