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Disease-modifying management aims to prevent deterioration and progression ofthe disease, not just relieve symptoms. Unfortunately, the development ofnecessary therapies is often hampered by the failure to recognize thepresymptomatic disease and limited understanding of disease development. Wepresent a generic solution for this problem by a methodology that allows theprediction of progression risk and morphology in individuals using a latentextrapolation optimization approach. To this end, we combined a regularizedgenerative adversarial network (GAN) and a latent nearest neighbor algorithmfor joint optimization to generate plausible images of future time points. Weevaluated our method on osteoarthritis (OA) data from a multi-centerlongitudinal study (the Osteoarthritis Initiative, OAI). With presymptomaticbaseline data, our model is generative and significantly outperforms theend-to-end learning model in discriminating the progressive cohort. Twoexperiments were performed with seven experienced radiologists. When nosynthetic follow-up radiographs were provided, our model performed better thanall seven radiologists. In cases where the synthetic follow-ups generated byour model were available, the specificity and sensitivity of all readers indiscriminating progressors increased from $72.3\%$ to $88.6\%$ and from$42.1\%$ to $51.6\%$, respectively. Our results open up a new possibility ofusing model-based morphology and risk prediction to make predictions aboutfuture disease occurrence, as demonstrated in the example of OA.

Image prediction of disease progression by style-based manifold extrapolation