Open AccessMachine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data
Author(s) -
Yunan Xu,
Yizi Lin,
Ryan P. Bell,
Sheri L. Towe,
J. M. Pearson,
Tauseef Nadeem,
Cliburn Chan,
Christina S. Meade
Publication year - 2021
Publication title -
journal of neurovirology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.868
H-Index - 85
eISSN - 1538-2443
pISSN - 1355-0284
DOI - 10.1007/s13365-020-00930-4
Subject(s) - magnetic resonance imaging , medicine , receiver operating characteristic , neuroimaging , artificial intelligence , logistic regression , white matter , cross validation , feature selection , support vector machine , machine learning , radiology , computer science , psychiatry
Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a prediction model for NCI among people with HIV using clinical- and magnetic resonance imaging (MRI)-derived features. The sample included 101 adults with chronic HIV disease. NCI was determined using a standardized neuropsychological testing battery comprised of seven domains. MRI features included gray matter volume from high-resolution anatomical scans and white matter integrity from diffusion-weighted imaging. Clinical features included demographics, substance use, and routine laboratory tests. Least Absolute Shrinkage and Selection Operator Logistic regression was used to perform variable selection on MRI features. These features were subsequently used to train a support vector machine (SVM) to predict NCI. Three different classification tasks were performed: one used only clinical features; a second used only selected MRI features; a third used both clinical and selected MRI features. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity with a tenfold cross-validation. The SVM classifier that combined selected MRI with clinical features outperformed the model using clinical features or MRI features alone (AUC: 0.83 vs. 0.62 vs. 0.79; accuracy: 0.80 vs. 0.65 vs. 0.72; sensitivity: 0.86 vs. 0.85 vs. 0.86; specificity: 0.71 vs. 0.37 vs. 0.52). Our results provide preliminary evidence that combining clinical and MRI features can increase accuracy in predicting NCI and could be developed as a potential tool for NCI diagnosis in HIV clinical practice.