CT放射组学在鉴别纵隔淋巴结结核与非小细胞肺癌纵隔淋巴结转移瘤中的应用价值  

The value of CT radiomics in differentiating the mediastinal lymph node tuberculosis and mediastinal lymph node metastasis of non-small cell lung cancer

作　　者：袁小记 孙秀彬 韩榕 倪聪慧 王武章 于德新[3] Yuan Xiaoji;Sun Xiubin;Han Rong;Ni Conghui;Wang Wuzhang;Yu Dexin(Department of Radiology,Shandong Public Health Clinical Center,Ji’nan 250102,China;Department of Biostatistics,School of Public Health,Cheeloo Collage of Medicine,Shandong University,Ji’nan 250012,China;Department of Radiology,Qilu Hospital of Shandong University,Ji’nan 250012,China)

机构地区：[1]山东省公共卫生临床中心医学影像部,济南250102 [2]山东大学齐鲁医学院公共卫生学院生物统计学系,济南250012 [3]山东大学齐鲁医院放射科,济南250012

出　　处：《中国防痨杂志》2023年第10期949-956,共8页Chinese Journal of Antituberculosis

摘　　要：目的:探讨CT放射组学分析技术在纵隔淋巴结结核与非小细胞肺癌纵隔淋巴结转移瘤中的价值。方法:采用回顾性研究方法,按照入组标准收集2017年9月至2021年11月山东省公共卫生临床中心和山东大学齐鲁医院确诊的109例纵隔淋巴结结核(结核组)和65例非小细胞肺癌纵隔淋巴结转移瘤(转移瘤组)患者的CT影像资料作为研究对象。利用双盲法对CT图像进行观测和勾画,利用Radcloud平台对勾画出的淋巴结感兴趣区域(VOI)提取放射组学特征。采用特征标准化方法、单因素和多因素logistic回归模型分析有鉴别诊断价值的特征及特征间共线性的影响。利用筛选出的放射组学特征,以山东省公共卫生临床中心的患者数据作为训练集,建立k-近邻判别法(KNN)、支持向量机(SVM)、极限梯度提升算法(XGBoost)、随机森林(RF)、logistic回归(LR)和决策树(DT)等6种机器学习方法的5折交叉验证模型,通过评价诊断效果来选择鉴别诊断效果最好的模型;再以山东大学齐鲁医院的患者数据作为验证集,对该模型的诊断效果进行组外验证。结果:174例患者的CT影像资料共勾画出281个VOI,结核组和转移瘤组分别有196个和85个,结核组每例患者分割出VOI中位数(四分位数)[1(1,8)个],明显高于转移瘤组[1(1,3)个],差异有统计学意义(Z=2.827,P=0.005)。共提取放射组学特征1409个,经特征标准化、单因素和多因素logistic回归分析后共筛选出相互独立的8个可用于建模的放射组学特征。采用训练组数据利用8个特征进行5折交叉验证建模诊断,发现SVM和LR模型的ROC曲线下面积(AUC)分别为0.834和0.821,优于其他4种模型;进而利用训练集数据建立LR和SVM模型,AUC值分别为0.809和0.911,再采用验证集数据进行组外验证,AUC值分别为0.804和0.851。结论:无论是否包含年龄和性别两个特征,放射组学数据所建立的LR模型和SVM模型在纵隔淋巴结结核与非Objective:To explore the value of CT radiomics in differentiating the mediastinal lymph node tuberculosis and mediastinal lymph node metastasis of non-small cell lung cancer.Methods:From September 2017 to November 2021,CT imaging data of 109 patients with mediastinal lymph node tuberculosis(tuberculosis group)and 65 patients with mediastinal lymph node metastasis of non-small cell lung cancer(metastasis group),who were diagnosed in Shandong Public Health Clinical Center and Qilu Hospital of Shandong University,were retrospectively collected.The CT images were observed and delineated with a double-blind method,and the radiomics features were extracted from the volume of interest(VOI)of the delineated lymph node by using the Radcloud platform.The feature normalization method,univariate and multivariate logistic regression models were used to analyze the characteristics with differential diagnosis capacity and the influence of collinearity between characteristics.Using the selected radiomics characteristics,the patient data of Shandong Public Health Clinical Center was used as a training set to establish a 5-fold cross-validation model of six machine learning methods(including k-Nearest Neighbor(KNN)),Support Vector Machine(SVM),eXtreme gradient boosting(XGBoost),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)),and the model with the best diagnostic effect was selected.Then the patient data of Qilu hospital of Shandong University was used as a validation set to verify the diagnostic effect of the model.Results:Two hundred and eighty-one VOIs on CT images were delineated in 174 patients including 196 VOIs in tuberculosis group and 85 ones in metastasis groups.The median(quartile)VOI(1(1,8))of the tuberculosis group was significantly higher than that of the metastasis group(1(1,3))(Z=2.827,P=0.005).A total of 1409 radiomics features were extracted,and eight mutually independent radiomics features were selected for modeling after feature standardization,univariate and multivariate logistic regression a

关 键 词：放射组学 结核 淋巴结 癌 非小细胞肺 转移瘤 诊断 鉴别 体层摄影术 X线计算机 

分 类 号：R52[医药卫生—内科学] R81[医药卫生—临床医学]