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首页> 外文期刊>Journal of Thoracic Disease >CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions
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CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions

机译：基于CT的放射学特征分析可预测前纵隔病变的风险

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Background: To retrospectively validate CT-based radiomics features for predicting the risk of anterior mediastinal lesions. Methods: 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. Results: 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%. Conclusions: 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.
机译：背景：回顾性验证基于CT的放射学特征，以预测前纵隔病变的风险。方法：回顾性研究截至2013年2月至2018年3月，对298例经病理证实为前纵隔病变的患者进行了回顾性研究。所有患者在治疗前均接受了CT扫描，包括130例未增强的计算机断层扫描（UECT）和168例对比增强CT（CECT）扫描。划定病变区域，并提取了总共1,029个放射学特征。使用最小绝对收缩和选择算子（Lasso）算法选择放射线学特征，这些特征与前纵隔的低风险病变的高风险的区分显着相关。然后，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，分别构建UECT和CECT扫描的放射学模型。根据接收器的工作特性（ROC）曲线评估了放射学特征的预测性能。结果：两个放射学分类器均包含最佳的12个放射学特征。就ROC曲线下的面积而言，使用放射线学模型区分高危病变与低危病变，CECT图像占74.1％，敏感性为66.67％，特异性为64.81％。同时，UECT图像为84.2％，灵敏度为71.43％，特异性为74.07％。结论：证实了两种建议的基于CT的放射学特征与前纵隔高，低风险病变的鉴别之间的关联，并且在风险分级方面，UECT扫描的放射学特征具有比CECT更好的预测性能。

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