Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised Variational Autoencoders

• Published in: IEEE journal of biomedical and health informatics 2022-12, Vol.26 (12), p.6024-6035
• Main Authors: Kmetzsch, Virgilio, Becker, Emmanuelle, Saracino, Dario, Rinaldi, Daisy, Camuzat, Agnes, Le Ber, Isabelle, Colliot, Olivier
• Format: Article
• Language: English
• Subjects: Amyotrophic lateral sclerosis; Artificial Intelligence; Bioengineering; Biological system modeling; Biomarkers; Clinical trials; Computer Science; Computer Vision and Pattern Recognition; Cross-Sectional Studies; Datasets; Deep learning; Degenerative diseases; Dementia disorders; Disease; Disease Progression; Disease progression score; Engineering Sciences; Frontotemporal dementia; Humans; Image Processing; Imaging; Life Sciences; Medical Imaging; microRNA; MicroRNAs; MicroRNAs - genetics; miRNA; multimodal data; Multimodal Imaging; Mutation; neurodegenerative disease; Neurodegenerative diseases; Neuroimaging; Ribonucleic acid; RNA; Signal and Image processing; Transcriptomics; variational autoencoder
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2022.3208517

Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40 presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison with state-of-the-art approaches. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.