Sui He, Liu Lin, Li Xuejia, Zuo Panli, Cui Jingjing, Mo Zhanhao
Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130033, China.
Huiying Medical Technology Co., Ltd., Beijing 100192, China.
J Thorac Dis. 2019 May;11(5):1809-1818. doi: 10.21037/jtd.2019.05.32.
To retrospectively validate CT-based radiomics features for predicting the risk of anterior mediastinal lesions.
A retrospective study was performed through February 2013 to March 2018 on 298 patients who had pathologically confirmed anterior mediastinal lesions. The patients all underwent CT scans before their treatment, including 130 unenhanced computed tomography (UECT) and 168 contrast-enhanced CT (CECT) scans. The lesion areas were delineated, and a total of 1,029 radiomics features were extracted. The least absolute shrinkage and selection operator (Lasso) algorithm method was used to select the radiomics features significantly associated with discrimination of high-risk from low-risk lesions in the anterior mediastinum. Then, 8-fold and 3-fold cross-validation logistic regression (LR) models were taken as the feature selection classifiers to build the radiomics models for UECT and CECT scan respectively. The predictive performance of the radiomics features was evaluated based on the receiver operating characteristics (ROC) curve.
Each of the two radiomics classifiers included the optimal 12 radiomic features. In terms of the area under ROC curve, using the radiomics model in discriminating high-risk lesions from the low-risks, CECT images accounted for 74.1% with a sensitivity of 66.67% and specificity of 64.81%. Meanwhile, UECT images were 84.2% with a sensitivity of 71.43% and specificity of 74.07%.
The association of the two proposed CT-based radiomics features with the discrimination of high and low-risk lesions in anterior mediastinum was confirmed, and the radiomics features of the UECT scan were proven to have better prediction performance than the CECT's in risk grading.
回顾性验证基于CT的影像组学特征对前纵隔病变风险的预测价值。
对2013年2月至2018年3月期间298例经病理证实的前纵隔病变患者进行回顾性研究。所有患者在治疗前均接受了CT扫描，其中130例为平扫CT（UECT），168例为增强CT（CECT）扫描。勾画出病变区域，共提取1029个影像组学特征。采用最小绝对收缩和选择算子（Lasso）算法方法，选择与前纵隔高风险和低风险病变鉴别显著相关的影像组学特征。然后，分别以8倍和3倍交叉验证逻辑回归（LR）模型作为特征选择分类器，构建UECT和CECT扫描的影像组学模型。基于受试者操作特征（ROC）曲线评估影像组学特征的预测性能。
两个影像组学分类器均包含最优的12个影像组学特征。在ROC曲线下面积方面，使用影像组学模型鉴别高风险和低风险病变时，CECT图像为74.1%，灵敏度为66.67%，特异度为64.81%。同时，UECT图像为84.2%，灵敏度为71.43%，特异度为74.07%。
证实了所提出的两个基于CT的影像组学特征与前纵隔高、低风险病变鉴别的相关性，且UECT扫描的影像组学特征在风险分级方面的预测性能优于CECT扫描。
