Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study

Lan Shang; Fang Wang; Yan Gao; Chaoxin Zhou; Jian Wang; Xinyue Chen; Aamer Rasheed Chughtai; Hong Pu; Guojin Zhang; Weifang Kong

doi:10.3389/fonc.2022.1043163

Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study

Front Oncol. 2022 Nov 24:12:1043163. doi: 10.3389/fonc.2022.1043163. eCollection 2022.

Authors

Lan Shang^{1

2}, Fang Wang³, Yan Gao⁴, Chaoxin Zhou⁴, Jian Wang⁵, Xinyue Chen⁶, Aamer Rasheed Chughtai⁷, Hong Pu^{1

2}, Guojin Zhang^{1

2}, Weifang Kong^{1

2}

Affiliations

¹ Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Affiliated Hospital of University of Electronic Science and Technology of China, Chengdu, China.
² Department of Radiology, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China.
³ Department of Radiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China.
⁴ Department of Radiology, The First People's Hospital of Liangshan Yi Autonomous Prefecture, Xichang, China.
⁵ Department of diagnostic imaging School of Computer Science, Nanjing University of Science and Technology, Nanjing, China.
⁶ Department of Diagnostic Imaging, Computed Tomography (CT) Collaboration, Siemens Healthineers, Chengdu, China.
⁷ Section of Thoracic Imaging, Cleveland Clinic Health System, Cleveland, OH, United States.

Abstract

Background: This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas.

Methods: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.

Results: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.

Conclusion: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.

Keywords: classifier; computed tomography; machine learning (ML); mediastinal cyst; radiomics; thymoma.