论文标题
从成像数据中扩大大脑疾病
Subtyping brain diseases from imaging data
论文作者
论文摘要
成像界越来越多地采用了机器学习（ML）方法，以提供与疾病诊断，预后和对治疗反应有关的个性化成像特征。临床神经科学和癌症成像是ML提供了特别有望的两个领域。然而，许多神经系统和神经精神疾病以及癌症通常在其临床表现，神经解剖学模式或遗传基础方面都是异质的。因此，在这种情况下，寻求单一疾病签名可能在提供个性化的精确诊断方面可能是无效的。当前一章的重点是使用成像数据寻求疾病亚型的ML方法，尤其是半监督的聚类。讨论了阿尔茨海默氏病及其前驱阶段，精神病，抑郁，自闭症和脑癌的工作。我们的目标是在方法和临床应用方面为读者提供广泛的概述。
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ML has offered particular promise. However, many neurologic and neuropsychiatric diseases, as well as cancer, are often heterogeneous in terms of their clinical manifestations, neuroanatomical patterns or genetic underpinnings. Therefore, in such cases, seeking a single disease signature might be ineffectual in delivering individualized precision diagnostics. The current chapter focuses on ML methods, especially semi-supervised clustering, that seek disease subtypes using imaging data. Work from Alzheimer Disease and its prodromal stages, psychosis, depression, autism, and brain cancer are discussed. Our goal is to provide the readers with a broad overview in terms of methodology and clinical applications.
