  Disease-modifying management aims to prevent deterioration and progression of
the disease, not just relieve symptoms. Unfortunately, the development of
necessary therapies is often hampered by the failure to recognize the
presymptomatic disease and limited understanding of disease development. We
present a generic solution for this problem by a methodology that allows the
prediction of progression risk and morphology in individuals using a latent
extrapolation optimization approach. To this end, we combined a regularized
generative adversarial network (GAN) and a latent nearest neighbor algorithm
for joint optimization to generate plausible images of future time points. We
evaluated our method on osteoarthritis (OA) data from a multi-center
longitudinal study (the Osteoarthritis Initiative, OAI). With presymptomatic
baseline data, our model is generative and significantly outperforms the
end-to-end learning model in discriminating the progressive cohort. Two
experiments were performed with seven experienced radiologists. When no
synthetic follow-up radiographs were provided, our model performed better than
all seven radiologists. In cases where the synthetic follow-ups generated by
our model were available, the specificity and sensitivity of all readers in
discriminating progressors increased from $72.3\%$ to $88.6\%$ and from
$42.1\%$ to $51.6\%$, respectively. Our results open up a new possibility of
using model-based morphology and risk prediction to make predictions about
future disease occurrence, as demonstrated in the example of OA.
