Keywords:
Artificial Intelligence, Computer applications, Breast, CAD, CT, Neural networks, Computer Applications-Detection, diagnosis, Screening
Authors:
X. Tian1, R. Zhang1, C. Xia1, S. Liang1, L. Yang2, Y. Zhu2; 1Beijing/CN, 2Guangzhou/CN
DOI:
10.26044/ecr2019/C-1202
Aims and objectives
Thymoma is a type of common anterior mediastinal tumor.
Evaluation of progress of thymoma is widely based on Masaoka-Koga staging system [1,
2].
Masaoka-Koga based classification for stage I from other stages accords to whether the thymoma breaks the tumor capsule or not.
The invasion of tumor capsule is an important factor related to prognosis.
Thus,
classification of stage I from stage II to IV is of great significance for patients' survival.
However,
it is not easy for radiologists to directly stage thymoma from images,
such as enhanced computed tomography (CT).
Moreover,
pre-operative staging approaches established through biopsy or surgical examination are also not widely adopted.
Deep learning has demonstrated great success in computer vision.
To increase the performance of thymoma staging from enhanced CT images,
we developed a deep learning approach to classify thymoma as stage I or stage II to IV according to Masakoa-Koga system preoperatively.