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Several studies have found that different quantitative MRI (qMRI) measurements are associated with the presence of Alzheimer's disease. Cognitive Assessment scores, Apolipoprotein E4 (ApoE4) and cerebrospinal fluid (CSF) biomarkers are important factors associated with the risk of conversion from MCI to AD. Despite this awareness, the relationship of the qMRI measurements with the conversion rate and their effect in multivariate survival models that combine Radiomics, CSF, ApoE4 and Cognitive assessment is not known. The objective of this work was to evaluate the importance of each data source using several machine learning(ML) approaches that build Cox Survival models that combine cognitive assessments, CSF, ApoE4 and qMRI features. 321 features from 442 subjects from the ADNI study that converted from the MCI status to AD were used. ML methods were explored in a Cross-validation framework. Test results indicated that cognitive assessments plus qMRI data produce Cox survival models that are 92% concordant with the conversion time from MCI to AD, while CSF biomarkers did not have a mayor contribution on the final survival Model.
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