A radiomics model predicts progression from mild cognitive impairment to alzheimer's disease using structural MRI.

The aim of this study is to build and validate a model based on structural magnetic resonance imaging (sMRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). A total of 343 patients with MCI were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database as study subjects. Among them, 154 patients progressed to AD during the 48-month follow-up. All subjects were randomly divided into a training set (n = 240) and a validation set (n = 103) in a 7:3 ratio according to enrollment time. The baseline T1-weighted (T1W) structural MR images of each patient were automatically segmented into whole-brain three-dimensional (3D) white and gray matter images based on the training set data. In addition, radiomics signatures were extracted from each structural image. Baseline neuropsychological scores were combined with the radiomics signatures to construct a prediction model using machine learning. The diagnostic accuracy and reliability of the model were evaluated using the receiver operating characteristic (ROC) curve analysis in both the training and validation sets. Stepwise logistic regression analysis showed that clinical dementia rating (CDR), Alzheimer's Disease Assessment Scale (ADAS-cog) and radiomics markers were independent predictors of progression from MCI to AD. ROC curve showed that the AUC values of CDR, ADAS-cog and radiomics markers in the training set and validation set were 0.895 and 0.882, respectively. The sensitivity was 0.933 and 0.977, and the specificity was 0.669 and 0.661, respectively. DeLong test showed that the diagnostic efficacy of the comprehensive model was significantly different from that of the independent predictors (P = 0.023). The integrated model, based on structural analysis of magnetic resonance images, can accurately identify and predict individuals with MCI at high risk of progressing to AD.