P. Scheltens, K. Blennow, and M. M. Breteler, Alzheimer's disease, Lancet, vol.388, pp.505-522, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01579097

S. J. Vos, F. Verhey, and L. Frolich, Prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage, Brain, vol.138, pp.1327-1365, 2015.

M. Kunneman, R. Pellittel, and F. H. Bouwman, Patients' and caregivers' views on conversations and shared decision making in diagnostic testing for Alzheimer's disease: the ABIDE project, Alzheimers Dement (N Y), vol.3, pp.314-336, 2017.

I. S. Van-maurik, M. D. Zwan, and B. M. Tijms, Interpreting biomarker results in individual patients with mild cognitive impairment in the Alzheimer's biomarkers in daily practice (ABIDE) project, JAMA Neurol, vol.74, pp.1481-91, 2017.

R. Handels, S. Vos, and M. G. Kramberger, Predicting progression to dementia in persons with mild cognitive impairment using cerebrospinal fluid markers, Alzheimers Dement, vol.13, pp.903-915, 2017.

A. C. Van-harten, P. J. Visser, and Y. A. Pijnenburg, Cerebrospinal fluid A?42 is the best predictor of clinical progression in patients with subjective complaints, Alzheimers Dement, vol.9, pp.481-87, 2013.

C. Davatzikos, P. Bhatt, L. M. Shaw, K. N. Batmanghelich, and J. Q. Trojanowski, Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification, Neurobiol Aging, vol.32, pp.2322-2341, 2011.

R. C. Petersen, O. Lopez, and M. J. Armstrong, Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology, Neurology, vol.90, pp.126-161, 2018.

G. B. Frisoni, M. Boccardi, and F. Barkhof, Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers, Lancet Neurol, vol.16, pp.661-76, 2017.

C. R. Jack, D. A. Bennett, and K. Blennow, NIAAA Research Framework: toward a biological definition of Alzheimer's disease, Alzheimers Dement, vol.14, pp.535-62, 2018.

P. Royston and D. G. Altman, External validation of a Cox prognostic model: principles and methods, BMC Med Res Methodol, vol.13, p.33, 2013.

W. M. Van-der-flier, Y. A. Pijnenburg, and N. Prins, Optimizing patient care and research: the Amsterdam Dementia Cohort, J Alzheimers Dis, vol.41, pp.313-340, 2014.

S. G. Mueller, M. W. Weiner, and L. J. Thal, Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimers Dement, vol.1, pp.55-66, 2005.

S. Palmqvist, H. Zetterberg, and N. Mattsson, Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease, Neurology, vol.85, pp.1240-1289, 2015.

P. J. Visser, F. R. Verhey, and M. Boada, Development of screening guidelines and clinical criteria for predementia Alzheimer's disease. The DESCRIPA study, Neuroepidemiology, vol.30, pp.254-65, 2008.

, For the Foundation for the National Institutes of Health see https://fnih.org For the complete list of ADNI investigators see, For the ADNI database see

S. Lovestone, P. Francis, and I. Kloszewska, AddNeuroMed-the European collaboration for the discovery of novel biomarkers for Alzheimer's disease, Ann N Y Acad Sci, vol.1180, pp.36-46, 2009.

J. Kornhuber, K. Schmidtke, and L. Frolich, Early and differential diagnosis of dementia and mild cognitive impairment: design and cohort baseline characteristics of the German Dementia Competence Network, Dement Geriatr Cogn Disord, vol.27, pp.404-421, 2009.

E. M. Arenazaurquijo, A. Bejanin, and J. Gonneaud, Association between educational attainment and amyloid deposition across the spectrum from normal cognition to dementia: neuroimaging evidence for protection and compensation, Neurobiol Aging, vol.59, pp.72-79, 2017.

S. Morbelli, A. Drzezga, and R. Perneczky, Resting metabolic connectivity in prodromal Alzheimer's disease. A European Alzheimer Disease Consortium (EADC) project, Neurobiol Aging, vol.33, pp.2533-50, 2012.

G. B. Frisoni, A. Prestia, and O. Zanetti, Markers of Alzheimer's disease in a population attending a memory clinic, Alzheimers Dement, vol.5, pp.307-324, 2009.

I. Santana, I. Baldeiras, and B. Santiago, Underlying biological processes in mild cognitive impairment: amyloidosis versus neurodegeneration, J Alzheimers Dis, vol.64, issue.1, pp.647-57, 2018.

T. T. Seppala, A. M. Koivisto, and P. Hartikainen, Longitudinal changes of CSF biomarkers in Alzheimer's disease, J Alzheimers Dis, vol.25, pp.583-94, 2011.

J. Maroco, D. Silva, and A. Rodrigues, Data mining methods in the prediction of dementia: a realdata comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests, BMC Res Notes, vol.4, p.299, 2011.

G. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. Moons, Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, BMJ, vol.350, p.7594, 2015.

M. W. Weiner, D. P. Veitch, P. S. Aisen, L. A. Beckett, N. J. Cairns et al., Impact of the Alzheimer's Disease Neuroimaging Initiative, Alzheimers Dement, vol.11, pp.865-84, 2004.

I. S. Van-maurik, L. Visser, and R. Pellittel, Development and usability of ADappt-an online tool to support clinicians, patients and caregivers in the diagnosis of mild cognitive impairment and Alzheimer's disease, JMIR Form Res, vol.3, p.13417, 2019.

A. De-wilde, M. M. Van-buchem, and R. Otten, Disclosure of amyloid positron emission tomography results to individuals without dementia: a systematic review, Alzheimers Res Ther, vol.10, p.72, 2018.

M. S. Albert, S. T. Dekosky, and D. Dickson, The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on AgingAlzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease, Alzheimers Dement, vol.7, pp.270-79, 2011.

I. S. Van-maurik, L. M. Van-der-kall, and A. De-wilde, Added value of amyloid PET in individualized risk predictions for MCI patients, Alzheimers Dement (Amst), vol.11, pp.529-566, 2019.

I. M. Lipkus, Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations, Med Decis Making, vol.27, pp.696-713, 2007.

E. Willemse, I. S. Maurik, and B. M. Tijms, Diagnostic performance of Elecsys immunoassays for cerebrospinal fluid Alzheimer's disease biomarkers in a nonacademic, multicentre memory clinic cohort: the ABIDE project, Alzheimers Dement (Amst), vol.10, pp.563-72, 2018.