Disease-modifying management aims to prevent deterioration and progression of the disease, and not just to relieve symptoms. We present a solution for the management by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation approach. To this end, we combined a regularized generative adversarial network and a latent nearest neighbour algorithm for joint optimization to generate plausible images of future time points. We evaluated our method on osteoarthritis data from a multicenter longitudinal study (the Osteoarthritis Initiative). With presymptomatic baseline data, our model is generative and considerably outperforms the end-to-end learning model in discriminating the progressive cohort. Two experiments were performed with seven radiologists. When no synthetic follow-up radiographs were provided, our model performed better than all seven radiologists. In cases in which the synthetic follow-ups generated by our model were made available to the radiologist for diagnosis support, 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 disease occurrence, as demonstrated by the example of osteoarthritis.

疾病缓解管理旨在防止疾病恶化和进展，而不仅仅是缓解症状。我们通过一种方法提出了一种管理解决方案，该方法允许使用潜在外推法预测个体的进展风险和形态。为此，我们将正则化生成对抗网络和潜在最近邻算法结合起来进行联合优化，以生成未来时间点的合理图像。我们根据多中心纵向研究（骨关节炎倡议）的骨关节炎数据评估了我们的方法。有了症状前的基线数据，我们的模型是生成的，并且在区分进步队列方面大大优于端到端学习模型。七名放射科医师进行了两项实验。当没有提供合成的后续放射照片时，我们的模型比所有七位放射科医生的表现都要好。在我们的模型生成的综合随访结果可​​供放射科医师用于诊断支持的情况下，所有读者在区分进展者方面的特异性和敏感性分别从 72.3% 增加到 88.6% 和从 42.1% 增加到 51.6%。我们的结果开辟了使用基于模型的形态学和风险预测来预测疾病发生的新可能性，如骨关节炎的例子所示。所有读者在区分进展者方面的特异性和敏感性分别从 72.3% 增加到 88.6% 和从 42.1% 增加到 51.6%。我们的结果开辟了使用基于模型的形态学和风险预测来预测疾病发生的新可能性，如骨关节炎的例子所示。所有读者在区分进展者方面的特异性和敏感性分别从 72.3% 增加到 88.6% 和从 42.1% 增加到 51.6%。我们的结果开辟了使用基于模型的形态学和风险预测来预测疾病发生的新可能性，如骨关节炎的例子所示。