Machine-learning-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours. Issue 126 (May 2020)

• Record Type: Journal Article
• Authors: Hu, Jianping; Zhao, Yijing; Li, Mengcheng; Liu, Yin; Wang, Feng; Weng, Qiang; You, Ruixiong; Cao, Dairong
• Is Part Of: European journal of radiology. Issue 126(2020)
• Publication Date: 2020-05
• Subjects: Radiomics -- Machine learning -- Thymic epithelial tumour -- Computed tomography -- WHO classification; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.108929
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Machine learning based radiomic analysis can be used for differentiating low-risk from high-risk TETs. The combination of UNCT and CECT radiomic features can improve the performance to distinguish high-risk from low-risk TETs. The classifier RF has the best performance with the AUC of 0.87. Abstract: Purpose: To evaluate the performance of machine-learning-based computed tomography (CT) radiomic analysis to differentiate high-risk thymic epithelial tumours (TETs) from low-risk TETs according to the WHO classification. Method: This retrospective study included 155 patients with a histologic diagnosis of high-risk TET (n = 72) and low-risk TET (n = 83) who underwent unenhanced CT (UECT) and contrast-enhanced CT (CECT). The radiomic features were extracted from the UECT and CECT of each patient at the largest cross-section of the lesion. The classification performance was evaluated with a nested leave-one-out cross-validation approach combining the least absolute shrinkage and selection operator feature selection and four classifiers: generalised linear model (GLM), k-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF). The receiver-operating characteristic curve (ROC) and the area under the curve (AUC) were used to evaluate the performance of the classifiers. Results: The combination of UECT and CECT radiomic features demonstrated the best performance to differentiate high-risk TETs from low-risk TETs for all four classifiers. Among these classifiers, the RF had the highest AUC of 0.87, followed by GLM (AUC = 0.86), KNN (AUC = 0.86) and SVM (AUC = 0.84). Conclusions: Machine learning-based CT radiomic analysis allows for the differentiation of high-risk TETs and low-risk TETs with excellent performance, representing a promising tool to assist clinical decision making in patients with TETs.