Subtyping brain diseases from imaging data
This work provides a methodological overview for researchers and clinicians dealing with heterogeneous diseases like Alzheimer's, psychosis, and brain cancer, but it is incremental as it reviews existing approaches rather than introducing new ones.
The paper addresses the challenge of disease heterogeneity in clinical neuroscience and cancer imaging by reviewing machine learning methods, particularly semi-supervised clustering, to identify disease subtypes from imaging data, aiming to improve individualized precision diagnostics.
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.