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

Abstract

Abstract Background Neuroimaging and fluid biomarkers are used in clinics to differentiate frontotemporal dementia (FTD) from Alzheimer’s disease (AD) and other neurodegenerative and non-neurodegenerative disorders. We implemented a machine learning (ML) algorithm that provides individual probabilistic scores for these patients based on magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. Methods We used a calibrated classifier with a Support Vector Machine with MRI data. We obtained group classifications and individual probabilities associated with group correspondence. We used the individual probabilities to address the clinical problem of confidence in the diagnosis. We investigated whether combining MRI and CSF levels of Neurofilament light (NfL) and 14-3-3 could improve the diagnosis confidence. Results 215 AD patients (65 ± 10 years, 137 women), 103 FTD patients (64 ± 8 years, 49 women), and 173 healthy controls (CTR) (59 ± 15 years, 106 women) were studied. With MRI data only, we obtained accuracies of 88% in the AD vs. healthy controls (CTR) classification, 87% for FTD vs. CTR, 82% for AD vs. FTD, and 80% when differentiating the three groups. A total of 74% of FTD and 73% of AD participants have a high (≥ 0.8) probability of accurate diagnosis in the FTD vs. AD comparison. Adding CSF-NfL and 14-3-3 levels slightly improved the accuracy and the number of patients in the high diagnosis confidence group. Conclusion We propose a ML algorithm that provides individual diagnostic probabilities, and we validate it using MRI and/or CSF data. Our solution holds promise towards clinical applications as support to clinical findings or in settings with limited access to expert diagnoses.