Ruiyun Zhang, Wolfgang Hohenforst-Schmidt, Claus Steppert, Zsolt Sziklavari, Christian Schmidkonz, Armin Atzinger, Torsten Kuwert, Thorsten Klink, William Sterlacci, Arndt Hartmann, Michael Vieth, Stefan Förster
{"title":"标准化的18F-FDG PET/CT放射学特征提供了treatment-naïve非小细胞肺癌患者PD-L1表达状态的信息。","authors":"Ruiyun Zhang, Wolfgang Hohenforst-Schmidt, Claus Steppert, Zsolt Sziklavari, Christian Schmidkonz, Armin Atzinger, Torsten Kuwert, Thorsten Klink, William Sterlacci, Arndt Hartmann, Michael Vieth, Stefan Förster","doi":"10.1055/a-1816-6950","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To study the relationship between standardized 18F-FDG PET/CT radiomic features and clinicopathological variables and programmed death ligand-1 (PD-L1) expression status in non-small cell lung cancer (NSCLC) patients.</p><p><strong>Methods: </strong>58 NSCLC patients with preoperative 18F-FDG PET/CT scans and postoperative results of PD-L1 expression were retrospectively analysed. A standardized, open-source software was used to extract 86 radiomic features from PET and low-dose CT images. Univariate analysis and multivariate logistic regression were used to find independent predictors of PD-L1 expression. The Area Under the Curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the ability of variables and their combination in predicting PD-L1 expression.</p><p><strong>Results: </strong>Multivariate logistic regression resulted in the PET radiomic feature GLRLM_LGRE (Odds Rate (OR): 0.300 vs 0.114, 95% confidence interval (CI): 0.096-0.931 vs 0.021-0.616, in NSCLC and adenocarcinoma respectively) and the CT radiomic feature GLZLM_SZE (OR: 3.338 vs 7.504, 95%CI: 1.074-10.375 vs 1.382-40.755, in NSCLC and adenocarcinoma respectively), being independent predictors of PD-L1 status. In NSCLC group, after adjusting for gender and histology, the PET radiomic feature GLRLM_LGRE (OR: 0.282, 95%CI: 0.085-0.936) remained an independent predictor for PD-L1 status. In the adenocarcinoma group, when adjusting for gender the PET radiomic feature GLRLM_LGRE (OR: 0.115, 95%CI: 0.021-0.631) and the CT radiomic feature GLZLM_SZE (OR: 7.343, 95%CI: 1.285-41.965) remained associated with PD-L1 expression.</p><p><strong>Conclusion: </strong>NSCLC and adenocarcinoma with PD-L1 expression show higher tumour heterogeneity. Heterogeneity-related 18F-FDG PET and CT radiomic features showed good ability to non-invasively predict PD-L1 expression.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":" ","pages":"385-393"},"PeriodicalIF":1.2000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer.\",\"authors\":\"Ruiyun Zhang, Wolfgang Hohenforst-Schmidt, Claus Steppert, Zsolt Sziklavari, Christian Schmidkonz, Armin Atzinger, Torsten Kuwert, Thorsten Klink, William Sterlacci, Arndt Hartmann, Michael Vieth, Stefan Förster\",\"doi\":\"10.1055/a-1816-6950\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To study the relationship between standardized 18F-FDG PET/CT radiomic features and clinicopathological variables and programmed death ligand-1 (PD-L1) expression status in non-small cell lung cancer (NSCLC) patients.</p><p><strong>Methods: </strong>58 NSCLC patients with preoperative 18F-FDG PET/CT scans and postoperative results of PD-L1 expression were retrospectively analysed. A standardized, open-source software was used to extract 86 radiomic features from PET and low-dose CT images. Univariate analysis and multivariate logistic regression were used to find independent predictors of PD-L1 expression. The Area Under the Curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the ability of variables and their combination in predicting PD-L1 expression.</p><p><strong>Results: </strong>Multivariate logistic regression resulted in the PET radiomic feature GLRLM_LGRE (Odds Rate (OR): 0.300 vs 0.114, 95% confidence interval (CI): 0.096-0.931 vs 0.021-0.616, in NSCLC and adenocarcinoma respectively) and the CT radiomic feature GLZLM_SZE (OR: 3.338 vs 7.504, 95%CI: 1.074-10.375 vs 1.382-40.755, in NSCLC and adenocarcinoma respectively), being independent predictors of PD-L1 status. In NSCLC group, after adjusting for gender and histology, the PET radiomic feature GLRLM_LGRE (OR: 0.282, 95%CI: 0.085-0.936) remained an independent predictor for PD-L1 status. In the adenocarcinoma group, when adjusting for gender the PET radiomic feature GLRLM_LGRE (OR: 0.115, 95%CI: 0.021-0.631) and the CT radiomic feature GLZLM_SZE (OR: 7.343, 95%CI: 1.285-41.965) remained associated with PD-L1 expression.</p><p><strong>Conclusion: </strong>NSCLC and adenocarcinoma with PD-L1 expression show higher tumour heterogeneity. Heterogeneity-related 18F-FDG PET and CT radiomic features showed good ability to non-invasively predict PD-L1 expression.</p>\",\"PeriodicalId\":94161,\"journal\":{\"name\":\"Nuklearmedizin. Nuclear medicine\",\"volume\":\" \",\"pages\":\"385-393\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nuklearmedizin. Nuclear medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1055/a-1816-6950\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/6/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuklearmedizin. Nuclear medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-1816-6950","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/6/29 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
目的:研究非小细胞肺癌(NSCLC)患者标准化18F-FDG PET/CT放射学特征与临床病理变量及程序性死亡配体-1 (PD-L1)表达状态的关系。方法:回顾性分析58例NSCLC患者术前18F-FDG PET/CT扫描及术后PD-L1表达结果。使用标准化的开源软件从PET和低剂量CT图像中提取86个放射学特征。采用单因素分析和多因素logistic回归寻找PD-L1表达的独立预测因子。采用受试者工作特征(ROC)曲线下面积(AUC)比较各变量及其组合预测PD-L1表达的能力。结果:多因素logistic回归结果显示,PET放射学特征GLRLM_LGRE(比值比(OR): 0.300 vs 0.114, 95%可信区间(CI): 0.096-0.931 vs 0.021-0.616,分别为NSCLC和腺癌)和CT放射学特征GLZLM_SZE (OR: 3.338 vs 7.504, 95%CI: 1.074-10.375 vs 1.382-40.755,分别为NSCLC和腺癌)是PD-L1状态的独立预测因子。在NSCLC组中,在调整性别和组织学后,PET放射学特征GLRLM_LGRE (OR: 0.282, 95%CI: 0.085-0.936)仍然是PD-L1状态的独立预测因子。在腺癌组中,当调整性别时,PET放射学特征GLRLM_LGRE (OR: 0.115, 95%CI: 0.021-0.631)和CT放射学特征GLZLM_SZE (OR: 7.343, 95%CI: 1.285-41.965)仍然与PD-L1表达相关。结论:PD-L1表达的非小细胞肺癌和腺癌具有较高的肿瘤异质性。异质性相关的18F-FDG PET和CT放射学特征显示出良好的无创预测PD-L1表达的能力。
Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer.
Purpose: To study the relationship between standardized 18F-FDG PET/CT radiomic features and clinicopathological variables and programmed death ligand-1 (PD-L1) expression status in non-small cell lung cancer (NSCLC) patients.
Methods: 58 NSCLC patients with preoperative 18F-FDG PET/CT scans and postoperative results of PD-L1 expression were retrospectively analysed. A standardized, open-source software was used to extract 86 radiomic features from PET and low-dose CT images. Univariate analysis and multivariate logistic regression were used to find independent predictors of PD-L1 expression. The Area Under the Curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the ability of variables and their combination in predicting PD-L1 expression.
Results: Multivariate logistic regression resulted in the PET radiomic feature GLRLM_LGRE (Odds Rate (OR): 0.300 vs 0.114, 95% confidence interval (CI): 0.096-0.931 vs 0.021-0.616, in NSCLC and adenocarcinoma respectively) and the CT radiomic feature GLZLM_SZE (OR: 3.338 vs 7.504, 95%CI: 1.074-10.375 vs 1.382-40.755, in NSCLC and adenocarcinoma respectively), being independent predictors of PD-L1 status. In NSCLC group, after adjusting for gender and histology, the PET radiomic feature GLRLM_LGRE (OR: 0.282, 95%CI: 0.085-0.936) remained an independent predictor for PD-L1 status. In the adenocarcinoma group, when adjusting for gender the PET radiomic feature GLRLM_LGRE (OR: 0.115, 95%CI: 0.021-0.631) and the CT radiomic feature GLZLM_SZE (OR: 7.343, 95%CI: 1.285-41.965) remained associated with PD-L1 expression.
Conclusion: NSCLC and adenocarcinoma with PD-L1 expression show higher tumour heterogeneity. Heterogeneity-related 18F-FDG PET and CT radiomic features showed good ability to non-invasively predict PD-L1 expression.