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CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions

	author = {He Sui and Lin Liu and Xuejia Li and Panli Zuo and Jingjing Cui and Zhanhao Mo},
	title = {CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions},
	journal = {Journal of Thoracic Disease},
	volume = {11},
	number = {5},
	year = {2019},
	keywords = {},
	abstract = {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.},
	issn = {2077-6624},	url = {}