Early prediction of putamen imaging features in HIV-associated neurocognitive impairment syndrome

To explore the correlation between the volume of putamen and brain cognitive impairment in patients with HIV and to predict the feasibility of early-stage HIV brain cognitive impairment through radiomics. Retrospective selection of 90 patients with HIV infection, including 36 asymptomatic neurocogni...

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Bibliographic Details
Published in:BMC neurology 2021-03, Vol.21 (1), p.106-106, Article 106
Main Authors: Qi, Yu, Xu, Man, Wang, Wei, Wang, Yuan-Yuan, Liu, Jiao-Jiao, Ren, Hai-Xia, Liu, Ming-Ming, Li, Rui-Li, Li, Hong-Jun
Format: Article
Language:English
Subjects:
Acquired immune deficiency syndrome
Age
AIDS
Asymptomatic neurocognitive impairment
Brain slice preparation
Cognition
Cognition disorders
Cognitive ability
Complications and side effects
Dementia
Executive function
HIV
HIV infection
Human immunodeficiency virus
Infections
Information processing
Language
Magnetic resonance imaging
Medical examination
Memory
Nervous system
Neuroimaging
Neurological disorders
Patients
Principal components analysis
Putamen
Radiomics
Risk factors
Short term memory
Software
Substance abuse treatment
Telencephalon
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Summary:To explore the correlation between the volume of putamen and brain cognitive impairment in patients with HIV and to predict the feasibility of early-stage HIV brain cognitive impairment through radiomics. Retrospective selection of 90 patients with HIV infection, including 36 asymptomatic neurocognitive impairment (ANI) patients and 54 pre-clinical ANI patients in Beijing YouAn Hospital. All patients received comprehensive neuropsychological assessment and MRI scanning. 3D Slicer software was used to acquire volume of interest (VOI) and radiomics features. Clinical variables and volume of putamen were compared between patients with ANI and pre-clinical ANI. The Kruskal Wallis test was used to analysis multiple comparisons between groups. The relationship between cognitive scores and VOI was compared using linear regression. For radiomics, principal component analysis (PCA) was used to reduce model overfitting and calculations and then a support vector machine (SVM) was used to build a binary classification model. For model performance evaluation, we used an accuracy, sensitivity, specificity and receiver operating characteristic curve (ROC). There were no significant differences in clinical variables between ANI group and pre-clinical-ANI group (P>0.05). The volume of bilateral putamen was significantly different between AHI group and pre-clinical group (P
ISSN:1471-2377
1471-2377
DOI:10.1186/s12883-021-02114-x