Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer’s disease with MRI and CSF biomarkers

Neuroimaging and fluid biomarkers are used to differentiate frontotemporal dementia (FTD) from Alzheimer's disease (AD). We implemented a machine learning algorithm that provides individual probabilistic scores based on magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. We investigated whether combining MRI and CSF levels could improve the diagnosis confidence. 215 AD patients, 103 FTD patients, and 173 healthy controls (CTR) were studied. With MRI data, we obtained an accuracy of 82 % for AD vs. FTD. A total of 74 % of FTD and 73 % of AD participants have a high probability of accurate diagnosis. Adding CSF-NfL and 14-3-3 levels improved the accuracy and the number of patients in the confidence group for differentiating FTD from AD. We obtain individual diagnostic probabilities with high precision to address the problem of confidence in the diagnosis. We suggest when MRI, CSF, or the combination are necessary to improve the FTD and AD diagnosis. This algorithm holds promise towards clinical applications as support to clinical findings or in settings with limited access to expert diagnoses.

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Publication Details

Journal
Neurobiology of Aging
Published
2024-08-30
DOI
https://doi.org/10.1016/j.neurobiolaging.2024.08.008
Citations
4
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
1.54

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article

Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer’s disease with MRI and CSF biomarkers

Neus Falgàs, Agnès Pérez‐Millan, Albert Lladó, Bertrand Thirion et al.
4 citations
Neurobiology of Aging
Dementia and Cognitive Impairment Research
1.54
article

Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer’s disease with MRI and CSF biomarkers

Neus Falgàs, Agnès Pérez‐Millan, Albert Lladó, Bertrand Thirion, Jordi Sarto, Jordi Juncà‐Parella, Mircea Balasa, Josep M. Augé, Raquel Sánchez‐Valle, Adrià Tort‐Merino, Roser Sala‐Llonch, Núria Bargalló, Sergi Borrego‐Écija, Beatríz Bosch, Anna Antonell
article en
4 citations

Abstract

Neuroimaging and fluid biomarkers are used to differentiate frontotemporal dementia (FTD) from Alzheimer's disease (AD). We implemented a machine learning algorithm that provides individual probabilistic scores based on magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. We investigated whether combining MRI and CSF levels could improve the diagnosis confidence. 215 AD patients, 103 FTD patients, and 173 healthy controls (CTR) were studied. With MRI data, we obtained an accuracy of 82 % for AD vs. FTD. A total of 74 % of FTD and 73 % of AD participants have a high probability of accurate diagnosis. Adding CSF-NfL and 14-3-3 levels improved the accuracy and the number of patients in the confidence group for differentiating FTD from AD. We obtain individual diagnostic probabilities with high precision to address the problem of confidence in the diagnosis. We suggest when MRI, CSF, or the combination are necessary to improve the FTD and AD diagnosis. This algorithm holds promise towards clinical applications as support to clinical findings or in settings with limited access to expert diagnoses.

Neurobiology of AgingVol. 144
École Polytechnique (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Instituto de Salud Carlos III (ES), Centro de Investigación Biomédica en Red de Salud Mental (ES), Hospital Clínic de Barcelona (ES), Institute for Research in Biomedicine (ES), CEA Paris-Saclay (FR), Fundació Clínic per a la Recerca Biomèdica (ES), Universitat de Barcelona (ES)
Fundación BBVA, European Commission, Agence Nationale de la Recherche, Generalitat de Catalunya, Ministerio de Ciencia e Innovación, Universitat de Barcelona, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Agència de Gestió d'Ajuts Universitaris i de Recerca, Instituto de Salud Carlos III, Agencia Estatal de Investigación, H2020 Research Infrastructures
Openalex Percentile: Top 16%
Dementia and Cognitive Impairment Research
1.54
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