A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases

Virgilio Kmetzsch; Emmanuelle Becker; Dario Saracino; Vincent Anquetil; Daisy Rinaldi; Agnès Camuzat; Thomas Gareau; Isabelle Le Ber; Olivier Colliot

doi:10.1117/12.2607250

4 April 2022 A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases

Virgilio Kmetzsch, Emmanuelle Becker, Dario Saracino, Vincent Anquetil, Daisy Rinaldi, Agnès Camuzat, Thomas Gareau, Isabelle Le Ber, Olivier Colliot

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Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120321E (2022) https://doi.org/10.1117/12.2607250
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Frontotemporal dementia (FTD) is a rare neurodegenerative disease, often of genetic origin, with no effective treatment. There is a substantial pathophysiological overlap with amyotrophic lateral sclerosis (ALS), mutations in the C9orf72 gene being their most common genetic cause. In these disorders, no single biomarker can accurately measure progression, thus it is crucial to combine complementary information from multiple modalities to evaluate new therapeutic interventions. In particular, neuroimaging and transcriptomic (microRNA) data have been shown to have value to track FTD and ALS progression. As these conditions are rare, large samples are not available, hence the need for methods to fuse multimodal data from small samples. In this paper, we propose a method for computing a disease progression score (DPS) from cross-sectional multimodal data, based on variational autoencoders (VAE). We show that unsupervised training leads to the estimation of meaningful latent spaces, where subjects with similar disease states are clustered together and from which a DPS may be inferred. Models were evaluated on 14 patients, 40 presymptomatic mutation carriers and 37 healthy controls from the PREV-DEMALS study. Since there is no ground truth for the DPS, we used the inferred scores to perform pairwise classification as a proxy metric. Presymptomatic subjects and patients were classified with an average area under the ROC curve of 0.83 and 0.94, respectively without and with feature selection. The proposed approach has the potential to leverage cross-sectional multimodal datasets with small sample sizes in order to objectively measure disease progression.

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Virgilio Kmetzsch, Emmanuelle Becker, Dario Saracino, Vincent Anquetil, Daisy Rinaldi, Agnès Camuzat, Thomas Gareau, Isabelle Le Ber, and Olivier Colliot "A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120321E (4 April 2022); https://doi.org/10.1117/12.2607250

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