PURPOSE: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. MATERIALS AND METHODS: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. RESULTS: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. CONCLUSION: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.

中文翻译：

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基于CT图像和临床病理特征预测PD-L1在非小细胞肺癌中表达的影像组学研究。

目的：通过基于 CT 图像和临床病理特征的影像组学研究，预测非小细胞肺癌 (NSCLC) 患者肿瘤细胞的程序性死亡配体 1 (PD-L1) 表达。材料与方法：本回顾性研究共收集了 390 例确诊的 NSCLC 患者，这些患者进行胸部 CT 扫描和免疫组织化学（IHC）检查肺肿瘤的 PD-L1 并有临床资料，将其分为两个队列，即训练（n = 260) 和验证 (n = 130) 队列。比较两个队列的临床病理特征。通过在 CT 图像上使用 ITK-snap 套件对肺肿瘤进行分割。使用内部纹理分析软件计算了分割图像中的 200 个放射组学特征，然后通过最小绝对收缩和选择算子 (LASSO) 回归过滤和最小化，以根据其在 IHC 结果中的 PD-L1 表达状态的相关性选择最佳放射组学特征并开发放射组学特征。通过使用多变量逻辑回归分析，将放射组学特征和临床病理学危险因素结合起来开发预测模型。生成受试者工作特征 (ROC) 曲线并计算曲线下面积 (AUC) 以预测训练和验证队列中的 PD-L1 表达。结果：在提取的 200 个放射组学特征中，选择了 9 个来开发放射组学特征。在单变量分析中，肺肿瘤的 PD-L1 表达与放射组学特征、组织学类型和组织学分级显着相关（分别为 p < 0.05）。然而，PD-L1 表达与性别、年龄、肿瘤位置、CEA 水平、TNM 分期和吸烟无关（p > 0.05）。对于 PD-L1 表达的预测，结合放射组学特征和临床病理特征的预测模型在训练和验证队列中的 AUC 分别为 0.829 和 0.848。结论：结合放射组学特征和临床危险因素的预测模型有可能促进 NSCLC 患者 PD-L1 表达的个体化预测，并确定可以从抗 PD-L1 免疫治疗中受益的患者。训练和验证队列中分别有 848 个。结论：结合放射组学特征和临床危险因素的预测模型有可能促进 NSCLC 患者 PD-L1 表达的个体化预测，并确定可以从抗 PD-L1 免疫治疗中受益的患者。训练和验证队列中分别有 848 个。结论：结合放射组学特征和临床危险因素的预测模型有可能促进 NSCLC 患者 PD-L1 表达的个体化预测，并确定可以从抗 PD-L1 免疫治疗中受益的患者。