Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/103262
Title: Combined Structural MR and Diffusion Tensor Imaging Classify the Presence of Alzheimer's Disease With the Same Performance as MR Combined With Amyloid Positron Emission Tomography: A Data Integration Approach
Authors: Agostinho, Daniel 
Caramelo, Francisco 
Moreira, Ana Paula 
Santana, Isabel 
Abrunhosa, Antero 
Castelo Branco, Miguel 
Keywords: Alzheimer’s disease (AD); DTI; MRI; ensemble learning; machine learning; multimodal classification; positron—emission tomography
Issue Date: 2021
Project: SAICTPAC/0010/2015 
CENTRO-01-0145-FEDER-000016/BIGDATIMAGE 
UID/4950/2020 
DSAIPA/DS/0041/2020 
Serial title, monograph or event: Frontiers in Neuroscience
Volume: 15
Abstract: Background: In recent years, classification frameworks using imaging data have shown that multimodal classification methods perform favorably over the use of a single imaging modality for the diagnosis of Alzheimer's Disease. The currently used clinical approach often emphasizes the use of qualitative MRI and/or PET data for clinical diagnosis. Based on the hypothesis that classification of isolated imaging modalities is not predictive of their respective value in combined approaches, we investigate whether the combination of T1 Weighted MRI and diffusion tensor imaging (DTI) can yield an equivalent performance as the combination of quantitative structural MRI (sMRI) with amyloid-PET. Methods: We parcellated the brain into regions of interest (ROI) following different anatomical labeling atlases. For each region of interest different metrics were extracted from the different imaging modalities (sMRI, PiB-PET, and DTI) to be used as features. Thereafter, the feature sets were reduced using an embedded-based feature selection method. The final reduced sets were then used as input in support vector machine (SVM) classifiers. Three different base classifiers were created, one for each imaging modality, and validated using internal (n = 41) and external data from the ADNI initiative (n = 330 for sMRI, n = 148 for DTI and n = 55 for PiB-PET) sources. Finally, the classifiers were ensembled using a weighted method in order to evaluate the performance of different combinations. Results: For the base classifiers the following performance levels were found: sMRI-based classifier (accuracy, 92%; specificity, 97% and sensitivity, 87%), PiB-PET (accuracy, 91%; specificity, 89%; and sensitivity, 92%) and the lowest performance was attained with DTI (accuracy, 80%; specificity, 76%; and sensitivity, 82%). From the multimodal approaches, when integrating two modalities, the following results were observed: sMRI+PiB-PET (accuracy, 98%; specificity, 98%; and sensitivity, 99%), sMRI+DTI (accuracy, 97%; specificity, 99%; and sensitivity, 94%) and PiB-PET+DTI (accuracy, 91%; specificity, 90%; and sensitivity, 93%). Finally, the combination of all imaging modalities yielded an accuracy of 98%, specificity of 97% and sensitivity of 99%. Conclusion: Although DTI in isolation shows relatively poor performance, when combined with structural MR, it showed a surprising classification performance which was comparable to MR combined with amyloid PET. These results are consistent with the notion that white matter changes are also important in Alzheimer's Disease.
URI: https://hdl.handle.net/10316/103262
ISSN: 1662-4548
DOI: 10.3389/fnins.2021.638175
Rights: openAccess
Appears in Collections:FMUC Medicina - Artigos em Revistas Internacionais
I&D CIBIT - Artigos em Revistas Internacionais
I&D ICNAS - Artigos em Revistas Internacionais

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