Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma
Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma
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DOI：
10.3389/fonc.2020.00028
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发表时间：
2020-01-31
影响因子：
4.7
通讯作者：
Guo, Yan
中科院分区：
文献类型：
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作者：
Hong, Duo;Xu, Ke;Guo, Yan
目的：为了开发和验证放射组学签名，以确定EGFR突变的患者与晚期lung adenocarcinosis.Methods：本研究涉及201例晚期肺腺癌（140在培训队列和61在验证队列）。对增强和非增强CT图像进行预处理后，进行人工分割，共提取396个特征。使用Lasso算法进行特征选择，使用6种机器学习方法构建放射组学模型。应用受试者工作特征（ROC）曲线分析评价不同数据和方法之间放射组学特征的性能。使用临床因素和放射组学特征建立诺模图，然后基于其区分能力和校准进行分析。结果：通过LASSO算法，共筛选出10个造影数据特征和11个非造影数据特征。在所有6种不同的机器学习方法中，对比图像的放射组学签名的性能优于非对比图像。最后，基于增强CT成像，使用逻辑回归方法建立了最佳放射组学特征，验证队列中的曲线下面积（AUC）为0.851（95%CI，0.750至0.951）。使用放射组学特征和性别开发列线图，训练队列的C指数为0.908（95%CI，0.862至0.954），验证队列的C指数为0.835（95%CI，0.825至0.845）。结论：放射组学特征可用于区分EGFR阳性和野生型晚期肺腺癌。
Purpose: To develop and validate a radiomic signature to identify EGFR mutations in patients with advanced lung adenocarcinoma.Methods: This study involved 201 patients with advanced lung adenocarcinoma (140 in the training cohort and 61 in the validation cohort). A total of 396 features were extracted from manual segmentation based on enhanced and non-enhance CT imaging after image preprocessing. The Lasso algorithm was used for feature selection, 6 machine learning methods were used to construct radiomics models. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the performance of the radiomic signature between different data and methods. A nomogram was developed using clinical factors and the radiomics signature, then it was analyzed based on its discriminatory ability and calibration. Decision curve analysis (DCA) was implemented to evaluate the clinical utility.Results: Ten features for contrast data and eleven features for non-contrast data were selected through LASSO algorithm. The performance of the radiomics signature for contrast images was better than that for non-contrast images in all of the 6 different machine learning methods. Finally, the best radiomics signature was built with logistic regression method based on enhanced CT imaging with an area under the curve (AUC) of 0.851 (95% CI, 0.750 to 0.951) in the validation cohort. A nomogram was developed using the radiomics signature and sex with a C-index of 0.908 (95%CI, 0.862 to 0.954) in the training cohort and 0.835 (95% CI, 0.825 to 0.845) in the validation cohort. It showed good discrimination and calibration (Hosmer-Lemeshow test, P = 0.621 for the training cohort and P = 0.605 for the validation cohort).Conclusion: Radiomics signature can help to distinguish between EGFR positive and wild type advanced lung adenocarcinomas.