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

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

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

Author(s) -

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

Publication year - 2020

Publication title -

annals of translational medicine

Language(s) - English

Resource type - Journals

eISSN - 2305-5847

pISSN - 2305-5839

DOI - 10.21037/atm.2020.02.183

Subject(s) - thymoma , stage (stratigraphy) , medicine , receiver operating characteristic , radiology , computed tomography , multivariate analysis , nuclear medicine , surgery , paleontology , biology

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.

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