CT放射组学分析空洞特征在鉴别非结核分枝杆菌肺病与肺结核中的价值  

Value of CT radiomics analysis of cavity characteristics in differentiating pulmonary disease of nontuberculous mycobacterium from tuberculosis

作　　者：阎庆虎 崔嘉[2] 杨传彬[2] 王武章 于德新[1] 柴象飞 YAN Qinghu;CUI Jia;YANG Chuanbin;WANG Wuzhang;YU Dexin;CHAI Xiangfei(Department of Radiology,Qilu Hospital of Shandong University,Jinan 250012,Shandong,China;Department of Radiology,Shandong Provincial Chest Hospital,Jinan 250013,Shandong,China;Huiying Medical Technology(Beijing)Co.,Ltd.,Beijing 100192,China)

机构地区：[1]山东大学齐鲁医院放射科,山东济南250012 [2]山东省胸科医院影像科,山东济南250013 [3]慧影医疗科技(北京)有限公司,北京100192

出　　处：《山东大学学报（医学版）》2020年第6期41-46,共6页Journal of Shandong University:Health Sciences

摘　　要：目的探讨计算机体层摄影(CT)放射组学分析技术在鉴别含空洞型非结核分枝杆菌(NTM)肺病与含有类似空洞的肺结核中的价值。方法回顾性分析2013年2月至2018年3月在山东省胸科医院和山东大学齐鲁医院经临床证实的空洞型NTM肺病患者51例和含有类似空洞的肺结核患者42例的临床资料。利用双盲法对CT图像进行观测和勾画,勾画出198个感兴趣区(VOI)空洞,使用计算机生成的随机数将80%的VOI分配给训练数据集,20%的VOI分配给验证数据集。利用Radcloud平台提取的1409个放射组学特征来分析两种疾病CT中空洞特征的差异,利用方差阈值法、K最佳方法及Lasso算法3种方法筛选最佳特征,采用3个受监督的学习分类器(KNN、SVM、DT)分析受试者工作特征(ROC)曲线。结果筛选出94个最佳特征,采用了不同学习分类器分析得到的ROC曲线值均较高。验证集AUC最低值为0.95,最高值达到1.00。验证集的灵敏度和特异度也达到了0.95,通过精度、召回率、F1评分和支持度分析的3种分类器的性能良好。结论利用CT放射组学提取出有价值的空洞特征可以弥补肉眼观察的不足,在NTM肺病与肺结核的鉴别中具有重要意义。Objective To analyze the value of computer tomography(CT)radiomics features on differentiating nontuberculous mycobacteria(NTM)lung diseases with cavity from pulmonary tuberculosis with similar cavity.Methods Clinical data of 51 pulmonary NTM patients and 42 pulmonary tuberculosis patients with similar cavity from February 2013 to March 2018 in Shandong Provincial Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed.Double-blind method was used to observe and sketch CT images,and 198 cavities of volume of interests(VOI)were drawn by two experienced radiologists,and then 80%of VOI cavities were allocated to training data set and 20%to verification data set by using random number generated by computer.A total of 1409 radiomics features extracted from Radcloud platform were used to analyze the differences in CT cavity characteristics of the two diseases.The best features were selected by variance threshold method,K best method and Lasso algorithm.The receiver operating characteristic(ROC)curves were analyzed by three supervised learning classifiers(KNN,SVM and DT).Results A total of 94 best features were selected.The different learning classifiers showed that the lowest and the highest AUC values of validation set were 0.95 and 1.00,respectively.The sensitivity and specificity of the verification set were more than 0.95.The fine performance of the three classifiers was obtained by using four indicators(precision,recall rate,F1 score and support degree).Conclusion Some valuable cavity features can be extracted via CT radiomics,and are helpful for the differential diagnosis between the pulmonary NTM and pulmonary tuberculosis,which may make up for the lack of visual observation of the common CT images.

关 键 词：放射组学 空洞 计算机体层摄影 非结核分枝杆菌肺病 肺结核 

分 类 号：R445.3[医药卫生—影像医学与核医学]