Sun Zongqiong, Hu Shudong, Ge Yuxi, Wang Jun, Duan Shaofeng, Song Jiayang, Hu Chunhong, Li Yonggang
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China.
Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, China.
J Xray Sci Technol. 2020;28(3):449-459. doi: 10.3233/XST-200642.
To predict programmed death-ligand 1 (PD-L1) expression of tumor cells in non-small cell lung cancer (NSCLC) patients by using a radiomics study based on CT images and clinicopathologic features.
A total of 390 confirmed NSCLC patients who performed chest CT scan and immunohistochemistry (IHC) examination of PD-L1 of lung tumors with clinic data were collected in this retrospective study, which were divided into two cohorts namely, training (n = 260) and validation (n = 130) cohort. Clinicopathologic features were compared between two cohorts. Lung tumors were segmented by using ITK-snap kit on CT images. Total 200 radiomic features in the segmented images were calculated using in-house texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features based on its relevance of PD-L1 expression status in IHC results and develop radiomics signature. Radiomics signature and clinicopathologic risk factors were incorporated to develop prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curves were generated and the areas under the curves (AUC) were reckoned to predict PD-L1 expression in both training and validation cohorts.
In 200 extracted radiomic features, 9 were selected to develop radiomics signature. In univariate analysis, PD-L1 expression of lung tumors was significantly correlated with radiomics signature, histologic type, and histologic grade (p < 0.05, respectively). However, PD-L1 expression was not correlated with gender, age, tumor location, CEA level, TNM stage, and smoking (p > 0.05). For prediction of PD-L1 expression, the prediction model that combines radiomics signature and clinicopathologic features resulted in AUCs of 0.829 and 0.848 in the training and validation cohort, respectively.
The prediction model that incorporates the radiomics signature and clinical risk factors has potential to facilitate the individualized prediction of PD-L1 expression in NSCLC patients and identify patients who can benefit from anti-PD-L1 immunotherapy.
通过基于CT图像和临床病理特征的放射组学研究，预测非小细胞肺癌（NSCLC）患者肿瘤细胞程序性死亡配体1（PD-L1）的表达情况。
本回顾性研究共纳入390例经确诊的NSCLC患者，这些患者均接受了胸部CT扫描、肺肿瘤PD-L1免疫组织化学（IHC）检查并提供了临床资料，将其分为两个队列，即训练队列（n = 260）和验证队列（n = 130）。比较两个队列的临床病理特征。使用ITK-snap工具包在CT图像上对肺肿瘤进行分割。使用内部纹理分析软件计算分割图像中的200个放射组学特征，然后通过最小绝对收缩和选择算子（LASSO）回归进行过滤和最小化，以根据其与IHC结果中PD-L1表达状态的相关性选择最佳放射组学特征，并建立放射组学特征模型。通过多变量逻辑回归分析，将放射组学特征模型和临床病理危险因素纳入，以建立预测模型。生成受试者操作特征（ROC）曲线，并计算曲线下面积（AUC），以预测训练队列和验证队列中的PD-L1表达情况。
在提取的200个放射组学特征中，选择了9个来建立放射组学特征模型。在单变量分析中，肺肿瘤的PD-L1表达与放射组学特征模型、组织学类型和组织学分级显著相关（p均<0.05）。然而，PD-L1表达与性别、年龄、肿瘤位置、癌胚抗原（CEA）水平、TNM分期和吸烟无关（p>0.05）。对于PD-L1表达的预测，结合放射组学特征模型和临床病理特征的预测模型在训练队列和验证队列中的AUC分别为0.829和0.848。
结合放射组学特征模型和临床危险因素的预测模型有潜力促进NSCLC患者PD-L1表达的个体化预测，并识别出可从抗PD-L1免疫治疗中获益的患者。
