Share Email Print
cover

Proceedings Paper • new

Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Various machine learning methods have been proposed for predicting progression of patients with mild cognitive impairment (MCI) to Alzheimer’s disease (AD) using neuroimaging data. Even though the vast majority of these works use the public dataset ADNI, reproducing their results is complicated because they often do not make available elements that are essential for reproducibility, such as selected participants and input data, image preprocessing and cross-validation procedures. Comparability is also an issue. Specially, the influence of different components like preprocessing, feature extraction or classification algorithms on the performance is difficult to evaluate. Finally, these studies rarely compare their results to models built from clinical data only, a critical aspect to demonstrate the utility of neuroimaging. In our previous work,1, 2 we presented a framework for reproducible and objective classification experiments in AD, that included automatic conversion of ADNI database into the BIDS community standard, image preprocessing pipelines and machine learning evaluation. We applied this framework to perform unimodal classifications of T1 MRI and FDG-PET images. In the present paper, we extend this work to the combination of multimodal clinical and neuroimaging data. All experiments are based on standard approaches (namely SVM and random forests). In particular, we assess the added value of neuroimaging over using only clinical data. We first demonstrate that using only demographic and clinical data (gender, education level, MMSE, CDR sum of boxes, RAVLT, ADASCog) results in a balanced accuracy of 76% (AUC of 0.85). This performance is higher than that of standard neuroimaging-based classifiers. We then propose a simple trick to improve the performance of neuroimaging-based classifiers: training from AD patients and controls (rather than from MCI patients) improves the performance of FDG-PET classification by 5 percent points, reaching the level of the clinical classifier. Finally, combining clinical and neuroimaging data, prediction results further improved to 79% balanced accuracy and an AUC of 0.89). These prediction accuracies, obtained in a reproducible way, provide a base to develop on top of it and, to compare against, more sophisticated methods. All the code of the framework and the experiments is publicly available at https: //gitlab.icm-institute.org/aramislab/AD-ML.

Paper Details

Date Published: 15 March 2019
PDF: 13 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490V (15 March 2019); doi: 10.1117/12.2512430
Show Author Affiliations
Jorge Samper-Gonzalez, National de Recherche en Informatique et en Automatique (France)
Institut du Cerveau et de la Moelle Épinière (France)
Sorbonne Univ. (France)
Ninon Burgos, Institut National de Recherche en Informatique et en Automatique (France)
Institut du Cerveau et de la Moelle Épinière (France)
Sorbonne Univ. (France)
Simona Bottani, Institut National de Recherche en Informatique et en Automatique (France)
Institut du Cerveau et de la Moelle Épinière (France)
Sorbonne Univ. (France)
Marie-Odile Habert, Lab. d'Imagerie Biomédicale (France)
Pitié-Salpêtrière Hospital (France)
Sorbonne Univ. (France)
Theodoros Evgeniou, INSEAD (France)
Stéphane Epelbaum, Pitié-Salpêtrière Hospital (France)
Sorbonne Univ. (France)
Olivier Colliot, Institut National de Recherche en Informatique et en Automatique (France)
Institut du Cerveau et de la Moelle Épinière (France)
Pitié-Salpêtrière Hospital (France)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top