CT影像组学联合形态学特征模型评估非小细胞肺癌患者预后生存期的价值  

Value of CT radiomics combined with morphological features in predicting the prognosis of patients with non-small cell lung cancer

作　　者：周洁 郑燕婷 江舒琪 安杰[1] 邱士军[1] SUWAL Sushant 黄绥丹 陈淮[2] 李翠[3] 方嘉琪 ZHOU Jie;ZHENG Yanting;JIANG Shuqi;AN Jie;QIU Shijun;SUWAL Sushant;HUANG Suidan;CHEN Huai;LI Cui;FANG Jiaqi(Department of Imaging,the First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou 510405,China;Department of Radiology,the Second Affiliated Hospital of Guangzhou Medical University,Guangzhou 510260,China;The First Clinical Medical College,Guangzhou University of Chinese Medicine,Guangzhou 510405,China)

机构地区：[1]广州中医药大学第一附属医院影像科,广东广州510405 [2]广州医科大学附属第二医院放射科,广东广州510260 [3]广州中医药大学第一临床医学院,广东广州510405

出　　处：《中国医学物理学杂志》2024年第1期18-26,共9页Chinese Journal of Medical Physics

基　　金：广东省自然科学基金(2022A1515011028)。

摘　　要：目的:探讨CT影像组学联合形态学特征模型对非小细胞肺癌患者预后生存时间的预测价值。方法:从癌症影像数据库(TCIA)下载并筛选出300例非小细胞肺癌患者资料(300个病灶),随机选取210例为训练集,90例为测试集。根据预后生存期将患者分为两个组,预后生存期≤3年为1组,预后生存期>3年为2组。用3D Slicer软件在CT图像病灶中逐层勾画得到感兴趣区(ROI),再从每个ROI中提取影像组学特征,采用t检验和最小绝对收缩与选择算子(LASSO)算法进行影像组学特征筛选。运用Logistic回归建立3种预测模型,包括影像组学模型、形态学模型和联合诊断模型。采用受试者工作特征(ROC)曲线评价3种预测模型效能。结果:影像组学标签、纵隔淋巴结转移在训练集和测试集的差异均具有统计学意义。影像组学模型、形态学模型和联合诊断模型,在训练集中ROC曲线下面积(AUC)分别为0.784(95%CI:0.722~0.847)、0.734(95%CI:0.664~0.804)、0.748(95%CI:0.680~0.815),在测试集中分别为0.737(95%CI:0.630~0.844)、0.665(95%CI:0.554~0.777)、0.687(95%CI:0.578~0.797)。影像组学模型诊断效能最佳。结论:CT影像组学模型能有效地预测非小细胞肺癌患者预后生存时间。Objective To explore the predictive value of CT radiomics and morphological features for the prognosis and survival in non-small cell lung cancer(NSCLC)patients.Methods The clinic data of 300 NSCLC patients(300 lesions)were downloaded from the Cancer Imaging Archive,with 210 randomly selected as the training set and 90 as the test set.According to the prognosis and survival,the patients were divided into two groups with survival period≤3 and>3 years.3D Slicer software was used to delineate the regions of interest layer by layer in CT images,and the radiomics features were extracted from each region of interest.Both t-test and least absolute shrinkage and selection operator were utilized for radiomics feature screening.Three types of prediction models,namely radiomics model,morphological model and combined model,were constructed with Logistic regression,whose performances were evaluated using the receiver operating characteristic(ROC)curve.Results The differences in radiomics labels and mediastinal lymph node metastasis between the training set and the test set were statistically significant.For radiomics model,morphological model and combined model,the area under the ROC curve was 0.784(95%CI:0.722-0.847),0.734(95%CI:0.664-0.804)and 0.748(95%CI:0.680-0.815)in the training set,and 0.737(95%CI:0.630-0.844),0.665(95%CI:0.554-0.777)and 0.687(95%CI:0.578-0.797)in the test set,which demonstrated that radiomics model had the best diagnostic performance.Conclusion The CT radiomics model can effectively predict the prognosis and survival in NSCLC patients.

关 键 词：非小细胞肺癌 影像组学 形态学特征 预后 生存 

分 类 号：R318[医药卫生—生物医学工程] R734.2[医药卫生—基础医学]