标题： 生存分析，无限高斯混合模型，FDG-PET和非影像数据在轻度认知障碍进展预测中的应用 显示英文标题

标题： Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment

摘要： 我们提出一种方法来发现 [18F] 氟脱氧葡萄糖正电子发射断层扫描 (PET) 扫描中的有趣脑区，同时也展示了当 PET 扫描与非影像变量结合使用时的好处。 具有区分性的脑区有助于更好地理解阿尔茨海默病 (AD) 的进展，也可以用于预测轻度认知障碍 (MCI) 向 AD 的转化。 引入了生存分析 (Cox 回归) 和无限高斯混合模型 (IGMM) 来识别有信息量的脑区，这些脑区可以进一步用于仅使用基线 PET 扫描来预测 MCI 向 AD 的转化（两年内）。 此外，当将非影像变量与识别出的有信息量的脑区一起使用时，预测准确性可以得到提高。 结果表明，PET 扫描影像数据比其他非影像数据更具预测性，当影像和非影像数据结合时甚至表现出更好的性能。 显示英文摘要

摘要： We present a method to discover interesting brain regions in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, showing also the benefits when PET scans are in combined use with non-imaging variables. The discriminative brain regions facilitate a better understanding of Alzheimer's disease (AD) progression, and they can also be used for predicting conversion from mild cognitive impairment (MCI) to AD. A survival analysis(Cox regression) and infinite Gaussian mixture model (IGMM) are introduced to identify the informative brain regions, which can be further used to make a prediction of conversion (in two years) from MCI to AD using only the baseline PET scan. Further, the predictive accuracy can be enhanced when non-imaging variables are used together with identified informative brain voxels. The results suggest that PET scan imaging data is more predictive than other non-imaging data, revealing even better performance when both imaging and non-imaging data are combined.