Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images

Lei Yang; Wenjia Cai; Xiaoyu Yang; Haoshuai Zhu; Zhenguo Liu; Xi Wu; Yiyan Lei; Jianyong Zou; Bo Zeng; Xi Tian; Rongguo Zhang; Honghe Luo; Ying Zhu

doi:10.21037/atm.2020.02.183

Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images

Ann Transl Med. 2020 Mar;8(6):287. doi: 10.21037/atm.2020.02.183.

Authors

Lei Yang¹, Wenjia Cai², Xiaoyu Yang³, Haoshuai Zhu¹, Zhenguo Liu¹, Xi Wu³, Yiyan Lei¹, Jianyong Zou¹, Bo Zeng¹, Xi Tian⁴, Rongguo Zhang⁴, Honghe Luo¹, Ying Zhu³

Affiliations

¹ Department of Thoracic Surgery, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
² State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
³ Department of Radiology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
⁴ Advanced Institute, Infervision, Beijing 100000, China.

Abstract

Background: Accurate thymoma staging via computed tomography (CT) images is difficult even for experienced thoracic doctors. Here we developed a preoperative staging tool differentiating Masaoka-Koga (MK) stage I patients from stage II patients using CT images.

Methods: CT images of 174 thymoma patients were retrospectively selected. Two chest radiologists independently assessed the images. Variables with statistical differences in univariate analysis were adjusted for age, sex, and smoking history in multivariate logical regression to determine independent predictors of the thymoma stage. We established a deep learning (DL) 3D-DenseNet model to distinguish the MK stage I and stage II thymomas. Furthermore, we compared two different methods to label the regions of interest (ROI) in CT images.

Results: In routine CT images, there were statistical differences (P<0.05) in contour, necrosis, cystic components, and the degree of enhancement between stage I and II disease. Multivariate logical regression showed that only the degree of enhancement was an independent predictor of the thymoma stage. The area under the receiver operating characteristic curve (AUC) of routine CT images for classifying thymoma as MK stage I or II was low (AUC =0.639). The AUC of the 3D-DenseNet model showed better performance with a higher AUC (0.773). ROIs outlined by segmentation labels performed better (AUC =0.773) than those outlined by bounding box labels (AUC =0.722).

Conclusions: Our DL 3D-DenseNet may aid thymoma stage classification, which may ultimately guide surgical treatment and improve outcomes. Compared with conventional methods, this approach provides improved staging accuracy. Moreover, ROIs labeled by segmentation is more recommendable when the sample size is limited.

Keywords: Masaoka-Koga stage (MK stage); Thymoma; X-ray computed tomography (X-ray CT); artificial intelligence (AI) (computer vision systems); neural networks.