Abstract
We propose a method to predict treatment response patterns based on spatio-temporal disease signatures extracted from longitudinal spectral domain optical coherence tomography (SD-OCT) images. We extract spatio-temporal disease signatures describing the underlying retinal structure and pathology by transforming total retinal thickness maps into a joint reference coordinate system. We formulate the prediction as a multi-variate sparse generalized linear model regression based on the aligned signatures. The algorithm predicts if and when recurrence of the disease will occur in the future. Experiments demonstrate that the model identifies predictive and interpretable features in the spatio-temporal signature. In initial experiments recurrence vs. non-recurrence is predicted with a ROC AuC of 0.99. Based on observed longitudinal morphology changes and a time-to-event based Cox regression model we predict the time to recurrence with a mean absolute error (MAE) of 1.25 months, comparing favorably to elastic net regression (1.34 months), demonstrating the benefit of a spatio-temporal survival model.
W.-D. Vogl—The financial support by the Austrian Federal Ministry of Economy, Family and Youth and the National Foundation for Research, Technology and Development, the EU (FP7-ICT-2009-5/318068, VISCERAL), and OeNB (15929) is gratefully acknowledged.
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Vogl, WD. et al. (2015). Spatio-Temporal Signatures to Predict Retinal Disease Recurrence. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_12
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