Chiang Sharon, Guindani Michele, Yeh Hsiang J, Dewar Sandra, Haneef Zulfi, Stern John M, Vannucci Marina
Department of Statistics, Rice University, Houston, TX, United States.
School of Medicine, Baylor College of Medicine, Houston, TX, United States.
Front Neurosci. 2017 Dec 5;11:669. doi: 10.3389/fnins.2017.00669. eCollection 2017.
We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.
我们开发了一种综合贝叶斯预测建模框架，该框架基于氟脱氧葡萄糖正电子发射断层扫描（PET）成像生物标志物的选择来识别个体病理性脑状态，并评估这些状态与临床结果的关联。我们考虑了一项针对颞叶癫痫（TLE）患者的研究数据，这些患者随后接受了前颞叶切除术。我们的建模框架将PET中观察到的区域葡萄糖代谢概况视为潜在个体病理状态的表型表现，假定该状态在人群中有所不同。我们采用的建模策略能够识别出以与感兴趣的临床结果有差异关联的潜在病理为特征的患者亚组。它还能识别出表征受试者病理状态的成像生物标志物。在数据应用中，我们识别出一组在前颞叶切除术后有高手术复发风险的TLE患者亚组，以及一组可用于区分潜在亚组的有鉴别力的脑区。我们表明，所提出的方法在预测术后癫痫复发方面实现了高交叉验证准确性。
