International audienceVarious machine learning approaches have been developed for predicting progression to Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI) from MRI and PET data. Objective comparison of these approaches is nearly impossible because of differences at all steps, from  data management to image processing and evaluation procedures. Moreover, with a few exceptions, these papers rarely compare their results to that obtained with clinical/cognitive data only, a critical point to demonstrate the practical utility of neuroimaging in this context. We previously proposed a framework for the reproducible evaluation of ML algorithms for AD classification. This framework was applied to AD classification using unimodal neuroimaging data (T1 MRI and FDG PET). Here, we extend our previous work  to the combination of multimodal clinical and neuroimaging data for predicting progression to AD among MCI patients. All the code is publicly available at: https://github.com/aramis-lab/AD-ML

Bottani, Simona

Burgos, Ninon

Colliot, Olivier

Epelbaum, Stephane

Evgeniou, Theodoros

Habert, Marie-Odile

Samper-Gonzalez, Jorge

International audienceVarious machine learning approaches have been developed for predicting progression to Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI) from MRI and PET data. Objective comparison of these approaches is nearly impossible because of differences at all steps, from  data management to image processing and evaluation procedures. Moreover, with a few exceptions, these papers rarely compare their results to that obtained with clinical/cognitive data only, a critical point to demonstrate the practical utility of neuroimaging in this context. We previously proposed a framework for the reproducible evaluation of ML algorithms for AD classification. This framework was applied to AD classification using unimodal neuroimaging data (T1 MRI and FDG PET). Here, we extend our previous work  to the combination of multimodal clinical and neuroimaging data for predicting progression to AD among MCI patients. All the code is publicly available at: https://gitlab.icm-institute.org/aramislab/AD-ML

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Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible study

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HAL Id: hal-02142315https://hal.inria.fr/hal-02142315Submitted on 28 May 2019HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Predicting progression to Alzheimer’s disease fromclinical and imaging data: a reproducible studyJorge Samper-Gonzalez, Ninon Burgos, Simona Bottani, Marie-Odile Habert,Theodoros Evgeniou, Stephane Epelbaum, Olivier ColliotTo cite this version:Jorge Samper-Gonzalez, Ninon Burgos, Simona Bottani, Marie-Odile Habert, Theodoros Evgeniou,et al.. Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproduciblestudy. OHBM 2019 - Organization for Human Brain Mapping Annual Meeting 2019, Jun 2019, Rome,Italy. ￿hal-02142315￿ Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible study  Jorge Samper-González​1​,  Ninon Burgos​1​,  Simona Bottani​1​, Marie-Odile Habert​2,3,4​, Theodoros Evgeniou​5​, Stéphane Epelbaum​1,6​, Olivier Colliot​1,6,7  1​ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris, France 2​Laboratoire d'Imagerie Biomédicale, Sorbonne Université, Inserm U 1146, CNRS UMR 7371,           Paris, France 3​AP-HP, Hôpital Pitié-Salpêtrière, Department of Nuclear Medicine, Paris, France 4​Centre Acquisition et Traitement des Images (CATI, cati-neuroimaging.com) 5​INSEAD, Bd de Constance, 77305 Fontainebleau, France 6​AP-HP, Department of Neuroradiology, Pitié-Salpêtrière Hospital, Paris, France 7​AP-HP, Department of Neurology, Pitié-Salpêtrière Hospital, Paris, France  Introduction Various machine learning approaches have been developed for predicting progression to           Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI) from MRI and PET              data (see e.g (Arbabshirani et al, 2017, Falahati et al, 2014, Rathore et al, 2017)). Objective                comparison of these approaches is nearly impossible because of differences at all steps, from              data management to image processing and evaluation procedures. Moreover, with a few            exceptions such as (Moradi et al, 2015), these papers rarely compare their results to that               obtained with clinical/cognitive data only, a critical point to demonstrate the practical utility of              neuroimaging in this context. We previously proposed a framework for the reproducible            evaluation of ML algorithms for AD classification (Samper-González et al, 2018). This            framework was applied to AD classification using unimodal neuroimaging data (T1 MRI and             FDG PET). Here, we extend our previous work to the combination of multimodal clinical and               neuroimaging data for predicting progression to AD among MCI patients.  All the code is publicly available at: ​https://gitlab.icm-institute.org/aramislab/AD-ML​.   Methods The data used was from the ADNI database. We included the same group of subjects as in                 (Samper-González et al, 2018), except 3 subjects who had missing cognitive tests. The             population is described in Figure 1. The framework comprises a modular set of tools from the Clinica software platform             (​www.clinica.run​) for: converting ADNI into BIDS format, feature extraction from T1 MRI and             FDG-PET data using tools from SPM, classification algorithms based on scikit-learn and            rigorous evaluation procedures. All features were corrected for the effect of age. First, we tested models using clinical/cognitive data alone, based on a random forest classifier              (RF). A first model used only gender, education level, MMSE and CDR-SB tests. We then               assessed the added value of RAVLT (a memory test) and ADAS-Cog. We finally added APOE               genotype. Secondly, we tested imaging data (T1 MRI and FDG PET) in isolation. In a first approach, a                 SVM model was trained to distinguish pMCI vs sMCI. In a second approach, it was trained to                 separate CN ABeta- (amyloid negative cognitively normal subjects) and AD ABeta+ (amyloid            positive AD patients) and applied to pMCI vs MCI classification. Finally, we integrated clinical/cognitive and imaging data. For imaging data, we used as feature              the continuous output of the SVM, which was combined with clinical/cognitive features into a RF               classifier.   Results Results obtained with the different approaches are presented in Figure 2. The simplest clinical              model performed poorly with a balanced accuracy (BAcc) of 0.66 and an area under the ROC                curve (AUC) of 0.73. Inclusion of either RAVLT or ADAS-Cog led to a strong improvement               (Bacc=0.75, AUC=0.84). Addition of APOE genotype did not improve the results. When trained             on pMCI vs sMCI, imaging data performed poorly (BAcc=0.67 for T1w MRI, BAcc=0.71 for              PET). Training on CN ABeta- vs AD ABeta+ substantially improved the results for FDG PET               (BAcc=0.76, AUC=0.82), the performance being close to that of clinical models. Finally,            combination of clinical and imaging data further improved the results, although moderately            (BAcc=0.79, AUC= 0.89).   Conclusions We demonstrated that using only clinical data allows reaching acceptable performance levels.            Integrating both imaging and clinical data can further improve the accuracy, but the             improvement remains moderate. We further showed that a simple trick (training on the easier              task CN ABeta- vs AD Beta+) improved imaged-based results. Moreover, using standard ML             approaches, we reached performances that are comparable to published sophisticated          algorithms (see e.g. (Rathore et al, 2017)). Overall, our results highlight the importance of systematically comparing image-based ML           approaches to a baseline using clinical data, an issue that is often overlooked in the               neuroimaging community.  References   (Arbabshirani et al, 2017) Arbabshirani, M.R., Plis, S., Sui, J., Calhoun, V.D., 2017. Single              subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145,            137–165. ​https://doi.org/10.1016/j.neuroimage.2016.02.079  (Falahati et al, 2014) Falahati, F., Westman, E., Simmons, A., 2014. Multivariate Data Analysis              and Machine Learning in Alzheimer’s Disease with a Focus on Structural Magnetic Resonance             Imaging. Journal of Alzheimer’s Disease 41, 685–708. ​https://doi.org/10.3233/JAD-131928  (Moradi et al, 2015) Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., Alzheimer’s               Disease Neuroimaging Initiative, 2015. Machine learning framework for early MRI-based          Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104, 398–412.         https://doi.org/10.1016/j.neuroimage.2014.10.002  (Rathore et al, 2017) Rathore S., Habes M., Iftikhar M.A., Shacklett A., Davatzikos C. (2017), ‘A                review on neuroimaging-based classification studies and associated feature extraction methods          for Alzheimer's disease and its prodromal stages’, Neuroimage, vol. 155, pp. 530-548  (Samper-González et al, 2018) Samper-González, J., Burgos, N., Bottani, S., Fontanella, S., Lu,             P., Marcoux, A., Routier, A., Guillon, J., Bacci, M., Wen, J., Bertrand, A., Bertin, H., Habert,                M.-O., Durrleman, S., Evgeniou, T., Colliot, O., Alzheimer’s Disease Neuroimaging Initiative,           Australian Imaging Biomarkers and Lifestyle flagship study of ageing, 2018. Reproducible           evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI            and PET data. ​183, pp 504-521​. Neuroimage. https://doi.org/10.1016/j.neuroimage.2018.08.042    Figures        

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Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible study

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