Diagnosis of Alzheimer's disease based on whole-brain radiomics

Abstract:

Objective: To analyze the value of radiomics，clinical and combined models for the classification of the cognitively normal（CN）group，mild cognitive impairment（MCI） group and Alzheimer’s disease（AD） group. Methods: Data of 139 CN cases，162 MCI patients and 128 AD patients with the clinical and imaging information were collected from Alzheimer’s Disease Neuroimaging Initiative（ADNI） database. Patients of every group were randomly divided into the training cohorts and validation cohorts with a rate of 7∶3. Radiomics features were extracted from the regions covering the cortex and subcortical nuclei based on 3D-T1WI MRI. Least absolute shrinkage and selection operator（LASSO） was used to select features and multivariate logistic regression was used to develop radiomics models based on the whole-brain cortex and subcortical nuclei. Clinical features were identified by univariate and multivariate logistic regression. The clinical model and the combined model fusing the radiomics features with the clinical risk factors were developed by multivariate logistic regression model. The receiver operating characteristics（ROC） curve was used to evaluate the performance of the classification models. Results: The area under the curve（AUC） of radiomics model in CN vs. AD，MCI vs. AD and CN vs. MCI were 0.975，0.957 and 0.929，respectively. The AUC of clinical model in the three groups were 0.839，0.675 and 0.685 respectively. The AUC of combined model fusing radiomics features and clinical variables in the three groups were 0.983，0.959 and 0.950，respectively. Conclusion: The classification and diagnosis of AD could be accurately conducted using the radiomics model based on the whole-brain cortex and subcortical nuclei and combined model. The combined model is the best recommended model to classify CN，MCI and AD.