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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/216298
Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer's disease with MRI and CSF biomarkers
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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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PÉREZ MILLAN, Agnès, THIRION, Bertrand, FALGÀS MARTÍNEZ, Neus, BORREGO ÉCIJA, Sergi, BOSCH CAPDEVILA, Beatriz, JUNCÀ PARELLA, Jordi, TORT MERINO, Adrià, SARTO ALONSO, Jordi, AUGÉ FRADERA, Josep maria, ANTONELL BOIXADER, Anna, BARGALLÓ ALABART, Núria, BALASA, Mircea, LLADÓ PLARRUMANÍ, Albert, SÁNCHEZ DEL VALLE DÍAZ, Raquel, SALA LLONCH, Roser. Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer's disease with MRI and CSF biomarkers. _Neurobiology of Aging_. 2024. Vol. 144, núm. 1-11. [consulta: 2 de febrer de 2026]. ISSN: 0197-4580. [Disponible a: https://hdl.handle.net/2445/216298]