目的·分析肥厚型心肌病（hypertrophic cardiomyopathy，HCM）患者与健康对照人群的心脏磁共振（cardiac magnetic resonance，CMR）短轴电影（cine）成像影像组学特征差异，并对2类人群进行分类。方法·纳入2018年1月—2021年12月就诊于上海交通大学医学院附属仁济医院心内科的HCM患者100例，以2∶1比例随机选取同时期健康对照（healthy control，HC）50例，在放射科医师规范操作下完成CMR检查。通过CVI 42后处理软件完成对入组人员左心室心功能及形态学的测量与评估，主要包括左心室射血分数（left ventricular ejection fraction，LVEF）、舒张末期左心室容积（left ventricular end-diastolic volume，LVEDV）和舒张末期左心室心肌质量（left ventricular end-diastolic mass，LVEDM）。并从CMR短轴电影成像中获取舒张末期心肌区域3D影像组学特征。分析影像组学特征在2类人群中的分布，并构建机器学习模型对2类人群进行分类。结果·共提取3D影像组学特征107个。在排除高度一致的特征后采用最小绝对值收敛和选择算子（least absolute shrinkage and selection operator，LASSO）模型进行5折交叉验证后，仍有11个系数非0的特征；利用K-best方法选择排序靠前的8个用于后续建模分析，其中4个特征在2组人群中差异具有统计学意义（均P<0.05）。随后构建支持向量机（support vector machine，SVM）和随机森林（random forest，RF）模型用于判别2类人群。结果显示：单一特征模型（一阶：熵）最大曲线下面积（area under the curve，AUC）为0.833（95%CI 0.695~0.968）；多特征模型（SVM算法）最高准确率为83.3%，其对应的AUC为0.882（95%CI 0.705~0.980）。结论·HCM患者与HC人群在左心室功能和左心室形态上均有显著差异，同时3D心肌影像组学特征也有显著差异。尽管单一特征模型可以鉴别2类人群，但联合多特征构建的模型有更好的分类效果。

Objective ·To analyze the differences and classify hypertrophic cardiomyopathy (HCM) patients and healthy controls (HC) using short-axis cine cardiac magnetic resonance (CMR) images-derived radiomics features. Methods ·One hundred HCM subjects were included, and fifty HC were randomly selected at 2∶1 ratio during January 2018 to December 2021 in the Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine. The CMR examinations were performed by experienced radiologists on these subjects. CVI 42 post-processing software was used to obtain left ventricular morphology and function measurements, including left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV) and left ventricular end-diastolic mass (LVEDM). The 3D radiomic features of the end-diastolic myocardial region were extracted from short-axis images CMR cine. The distribution of the radiomic features in the two groups was analysed and machine learning models were constructed to classify the two groups. Results ·One hundred and seven 3D radiomic features were selected and extracted. After exclusion of highly correlated features, least absolute shrinkage and selection operator (LASSO) was used, and a 5-fold cross-validation was performed. There were still 11 characteristics with non-zero coefficients. The K-best method was used to decide the top 8 features for subsequent analysis. Among them, four features were significantly different between the two groups (all P<0.05). Support vector machine (SVM) and random forest (RF) models were constructed to discriminate the two groups. The results showed that the maximum area under the curve (AUC) for the single-feature model (first order grayscale: entropy) was 0.833 (95%CI 0.685?0.968) and the maximum accuracy for the multi-feature model was 83.3% with an AUC of 0.882 (95%CI 0.705?0.980). Conclusion ·There are significant differences in both left ventricular function and left ventricular morphology between HCM and HC. The 3D myocardial radiomic features of the two groups are also significantly different. Although single feature is able to distinguish the two groups, the combination of multi-features show better classification performance.