A Bayesian Nonparametric Model for Disease Subtyping: Application to Emphysema Phenotypes.
A Bayesian Nonparametric Model for Disease Subtyping: Application to Emphysema Phenotypes.
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贝叶斯非参数模型用于疾病亚型：应用于肺气肿表型。
DOI：
10.1109/tmi.2016.2608782
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发表时间：
2017-01
影响因子：
10.6
通讯作者：
Jose Estepar RS
中科院分区：
文献类型：
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作者：
Ross JC;Castaldi PJ;Cho MH;Chen J;Chang Y;Dy JG;Silverman EK;Washko GR;Jose Estepar RS
我们介绍了一种新的贝叶斯非参数模型，使用疾病的亚型识别的疾病轨迹的概念。虽然我们的模型是通用的，我们证明，通过治疗的组成数据的医学图像中的组织模式的分数，我们的模型可以应用于研究人口亚组之间的不同的进展趋势。具体来说，我们将我们的算法应用于COPDGene研究中胸部CT扫描获得的定量肺气肿测量，并显示了几种不同的进展模式。由于肺气肿是慢性阻塞性肺疾病（COPD）的主要组成部分之一，COPD是美国第三大死亡原因，因此肺气肿和COPD亚型的改进定义非常有趣。我们研究了几个模型与我们的算法，并表明，一个与年龄，包年（香烟暴露的措施），吸烟状态作为预测因子之间的最佳折衷估计的预测性能和模型的复杂性。该模型确定了9种亚型，这些亚型与已知与COPD相关的7种单核苷酸多态性（SNP）显着相关。此外，该模型提供了更好的预测精度比多，多变量普通最小二乘回归证明在五重交叉验证分析。我们认为，我们的亚型算法的贡献，可以应用到桥梁之间的差距差距CT水平的评估组织组成的人口水平的分析组成的趋势，不同的疾病亚型。
We introduce a novel Bayesian nonparametric model that uses the concept of disease trajectories for disease subtype identification. Although our model is general, we demonstrate that by treating fractions of tissue patterns derived from medical images as compositional data, our model can be applied to study distinct progression trends between population subgroups. Specifically, we apply our algorithm to quantitative emphysema measurements obtained from chest CT scans in the COPDGene Study and show several distinct progression patterns. As emphysema is one of the major components of chronic obstructive pulmonary disease (COPD), the third leading cause of death in the United States, an improved definition of emphysema and COPD subtypes is of great interest. We investigate several models with our algorithm, and show that one with age, pack years (a measure of cigarette exposure), and smoking status as predictors gives the best compromise between estimated predictive performance and model complexity. This model identified nine subtypes which showed significant associations to seven single nucleotide polymorphisms (SNPs) known to associate with COPD. Additionally, this model gives better predictive accuracy than multiple, multivariate ordinary least squares regression as demonstrated in a five-fold cross validation analysis. We view our subtyping algorithm as a contribution that can be applied to bridge the gap between CT-level assessment of tissue composition to population-level analysis of compositional trends that vary between disease subtypes.