BACKGROUND Coronary artery calcium scoring only represents a small fraction of all information available in noncontrast cardiac computed tomography (CAC-CT). We hypothesized that an automated pipeline using radiomics and machine learning could identify phenotypic information about high-risk left ventricular hypertrophy (LVH) embedded in CAC-CT. METHODS This was a retrospective analysis of 1982 participants from the DHS (Dallas Heart Study) who underwent CAC-CT and cardiac magnetic resonance. Two hundred twenty-four participants with high-risk LVH were identified by cardiac magnetic resonance. We developed an automated adaptive atlas algorithm to segment the left ventricle on CAC-CT, extracting 107 radiomics features from the volume of interest. Four logistic regression models using different feature selection methods were built to predict high-risk LVH based on CAC-CT radiomics, sex, height, and body surface area in a random training subset of 1587 participants. RESULTS The respective areas under the receiver operating characteristics curves for the cluster-based model, the logistic regression model after exclusion of highly correlated features, and the penalized logistic regression models using least absolute shrinkage and selection operators with minimum or one SE λ values were 0.74 (95% CI, 0.67-0.82), 0.74 (95% CI, 0.67-0.81), 0.76 (95% CI, 0.69-0.83), and 0.73 (95% CI, 0.66-0.80) for detecting high-risk LVH in a distinct validation subset of 395 participants. CONCLUSIONS Ventricular segmentation, radiomics features extraction, and machine learning can be used in a pipeline to automatically detect high-risk phenotypes of LVH in participants undergoing CAC-CT, without the need for additional imaging or radiation exposure. Registration: URL http://www.clinicaltrials.gov. Unique identifier: NCT00344903.

中文翻译：

---

钙评分心脏计算机断层扫描扫描对高危左心室肥大的识别：在DHS中的验证。

背景技术冠状动脉钙积分仅占无对比心脏计算机断层扫描（CAC-CT）中所有可用信息的一小部分。我们假设使用放射线学和机器学习的自动化管道可以识别有关嵌入CAC-CT的高风险左心室肥大（LVH）的表型信息。方法这是对1982年DHS（达拉斯心脏研究）参与者进行CAC-CT和心脏磁共振检查的回顾性分析。通过心脏磁共振检查确定了224位高危LVH参与者。我们开发了一种自动自适应图集算法，以在CAC-CT上分割左心室，从感兴趣的体积中提取107个放射学特征。建立了四个使用不同特征选择方法的逻辑回归模型，以基于1587名参与者的随机训练子集中的CAC-CT放射学，性别，身高和体表面积预测高危LVH。结果基于聚类的模型，排除高度相关特征后的逻辑回归模型以及使用最小绝对收缩和最小或一个SEλ值的选择算子的惩罚逻辑回归模型的接收器工作特性曲线下的各个区域为0.74 （95％CI，0.67-0.82），0.74（95％CI，0.67-0.81），0.76（95％CI，0.69-0.83）和0.73（95％CI，0.66-0.80）用于检测高危LVH 395名参与者的独特验证子集。结论心室分割，放射学特征提取，并且可以在管道中使用机器学习来自动检测接受CAC-CT的参与者的LVH高风险表型，而无需额外的成像或放射线照射。注册：URL http://www.clinicaltrials.gov。唯一标识符：NCT00344903。