Developments in MRI radiomics research for vascular cognitive impairment

Abstract

Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decline induced by substantive brain damage following pathological changes in the cerebrovascular system. The primary clinical manifestations include behavioral abnormalities and diminished learning and memory cognitive functions. If the location and extent of brain injury are not identified early and therapeutic interventions are not promptly administered, it may lead to irreversible cognitive impairment. Therefore, the early diagnosis of VCI is crucial for its prevention and treatment. Prior to the onset of cognitive impairment in VCI, magnetic resonance imaging (MRI) radiomics can be utilized for early assessment and diagnosis, thereby guiding clinicians in providing precise treatment for patients, which holds significant potential for development. This article reviews the classification of VCI, the concept of radiomics, the application of MRI radiomics in VCI, and the limitations of radiomics in the context of advancements in its application within the central nervous system. CRITICAL RELEVANCE STATEMENT: This article explores how MRI radiomics can be used to detect VCI early, enhancing clinical radiology practice by offering a reliable method for prediction, diagnosis, and identification, which also promotes standardization in research and integration of disciplines. KEY POINTS: MRI radiomics can predict VCI early. MRI radiomics can diagnose VCI. MRI radiomics distinguishes VCI from Alzheimer's disease.

Keywords: Artificial intelligence; Magnetic resonance imaging; Radiomics; Vascular cognitive impairment; White matter lesions.

Fig. 1

Imaging radiomics flowchart. A MRI image acquisition refers to obtaining standard MRI image data, which is the foundation of radiomics. These data mainly come from medical institutions or public datasets. B Image segmentation is the process of segmenting an image into specific brain regions with biological significance or extracting regions of interest. C Feature extraction is the step of identifying quantitative features from segmented images that are helpful for disease diagnosis and analysis. D To avoid overfitting and improve accuracy, it is necessary to select the most relevant features to the disease state from a large number of extracted features before modeling. E The ultimate goal of radiomics is to establish a model for diagnosis, classification, and prediction

Fig. 2

Feature selection classification algorithm

Fig. 3

MRI imaging radiomics flowchart of VCI

Fig. 4

The process of study inclusion based on PRISMA methodology

Fig. 5

The research process of Feng et al