MRI Radiomics Nomogram for Predicting Disease Transition Time and Risk Stratification in Preclinical Alzheimer's...

Accurate prediction of the progression of preclinical Alzheimer's disease (AD) is crucial for improving clinical management and disease prognosis. The objective of this study was to develop and validate clinical-radimoics integrated model to predict the time to progression (TTP) and disease risk stratification of preclinical AD.

A total of 244 cases (mean age: 73.8 ± 5.5 years, 120 women) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly divided into the training cohort (n = 172) and validation cohort (n = 72) using a 7:3 ratio. Clinical factors were identified by univariate and multivariate COX regression. Radiomics features were extracted from GM, WM and CSF of T₁WI images and selected by Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO). Using selected clinical factors and radiomics features, the clinical, radimocis and clinical-radiomics nomogram models were developed for predicting the TTP. The performance of each model was assessed by C-index. The risk stratification ability and predicting efficacy of the clinical-radiomics model were utilizing the Kaplan-Meier curve and receiver operator characteristic (ROC) curve.

The C-index of clinical, radimocis and clinical-radiomics models were 0.852 (95% confidence interval[CI]:0.810-0.893), 0.863 (95%CI:0.816-0.910) and 0.903 (95%:0.870-0.936) in the training cohort and 0.725 (95%CI:0.630-0.820), 0.788 (95%CI:0.678-0.898), 0.813(95%CI:0.734-0.892) in the validation cohort. The AUCs of the multi-predictor nomogram at 1-, 3-, 5- and 7-year were 0.894, 0.908, 0.930, 0.979 in the training cohort and 0.671, 0.726, 0.839, 0.931 in the validation cohort.

In this study, we constructed a clinical-radimoics integrated model to predict the progression of preclinical AD and stratified the risk of disease progression in preclinical AD.