Literature DB >> 26089525

Associations between computed tomography features of thymomas and their pathological classification.

Yoshiyuki Ozawa1, Masaki Hara2, Masashi Shimohira1, Keita Sakurai1, Motoo Nakagawa1, Yuta Shibamoto1.   

Abstract

Background Thymoma exhibits a range of histological and biological features and their imaging findings varies. Purpose To evaluate the associations between CT findings of thymomas and their classification according to the Masaoka staging system and World Health Organization (WHO) classification. Material and Methods Eighty-four patients with thymoma were evaluated. Comparisons between the CT findings of Masaoka stage I/II and III/IV lesions, and the WHO type A-B1 (low risk) and B2/B3 (high risk) lesions were performed. Results Stage III/IV thymomas (mean size, 60 mm) were significantly larger than stage I/II (45 mm) lesions and had more irregular shape and contour. Necrosis and calcification were observed in 16 (59%) and nine (33%) stage III/IV thymomas, and 16 (28%) and seven (12%) stage I/II lesions, respectively. Regarding the WHO classification, the high-risk thymomas displayed irregular shape and contour more often than low-risk lesions. There were significant differences between the patterns of mediastinal invasion seen in high- and low-risk groups; 21 (68%) vs. six (12%) lesions demonstrated mediastinal fat invasion, seven (23%) vs. two (4%) lesions exhibited great vessel invasion, five (16%) vs. 0 (0%) lesions displayed pericardial invasion, and 18 (58%) vs. 10 (20%) lesions invaded the lungs, respectively. Conclusion Masaoka stage III/IV thymomas were larger in size, had more irregular shape and contour, and exhibited necrosis and calcification more often than the stage I/II lesions. In the WHO classification, high-risk thymomas demonstrated more irregular shape and contour than low-risk thymomas.

Entities:  

Keywords:  Masaoka stage; Thorax; computed tomography (CT); mediastinum; thymoma

Mesh:

Year:  2016        PMID: 26089525     DOI: 10.1177/0284185115590288

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  13 in total

1.  Multilobulated thymoma with an acute angle: a new predictor of lung invasion.

Authors:  Daniel B Green; Sarah Eliades; Alan C Legasto; Gulce Askin; Jeffrey L Port; James F Gruden
Journal:  Eur Radiol       Date:  2019-02-26       Impact factor: 5.315

2.  Using CT to evaluate mediastinal great vein invasion by thymic epithelial tumors: measurement of the interface between the tumor and neighboring structures.

Authors:  Shoji Kuriyama; Kazuhiro Imai; Koichi Ishiyama; Shinogu Takashima; Maiko Atari; Tsubasa Matsuo; Yoshiaki Ishii; Yuzu Harata; Yusuke Sato; Satoru Motoyama; Kyoko Nomura; Manabu Hashimoto; Yoshihiro Minamiya
Journal:  Eur Radiol       Date:  2021-09-23       Impact factor: 5.315

3.  Computed Tomography Features associated With the Eighth Edition TNM Stage Classification for Thymic Epithelial Tumors.

Authors:  Sukhmani K Padda; Donato Terrone; Lu Tian; Amanda Khuong; Joel W Neal; Jonathan W Riess; Mark F Berry; Chuong D Hoang; Bryan M Burt; Ann N Leung; Erich J Schwartz; Joseph B Shrager; Heather A Wakelee
Journal:  J Thorac Imaging       Date:  2018-05       Impact factor: 3.000

4.  Preoperative misdiagnosis analysis and accurate distinguish intrathymic cyst from small thymoma on computed tomography.

Authors:  Xin Li; Xingpeng Han; Wei Sun; Meng Wang; Guohui Jing; Xun Zhang
Journal:  J Thorac Dis       Date:  2016-08       Impact factor: 2.895

5.  Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters.

Authors:  Bo Li; Yong-Kang Xin; Gang Xiao; Gang-Feng Li; Shi-Jun Duan; Yu Han; Xiu-Long Feng; Wei-Qiang Yan; Wei-Cheng Rong; Shu-Mei Wang; Yu-Chuan Hu; Guang-Bin Cui
Journal:  Eur Radiol       Date:  2019-03-15       Impact factor: 5.315

6.  CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: "Impact of surgical modality choice".

Authors:  Ayten Kayi Cangir; Kaan Orhan; Yusuf Kahya; Hilal Özakıncı; Betül Bahar Kazak; Buse Mine Konuk Balcı; Duru Karasoy; Çağlar Uzun
Journal:  World J Surg Oncol       Date:  2021-05-11       Impact factor: 2.754

7.  Relationship Between Computed Tomography Imaging Features and Clinical Characteristics, Masaoka-Koga Stages, and World Health Organization Histological Classifications of Thymoma.

Authors:  Xiaowei Han; Wenwen Gao; Yue Chen; Lei Du; Jianghui Duan; Hongwei Yu; Runcai Guo; Lu Zhang; Guolin Ma
Journal:  Front Oncol       Date:  2019-10-11       Impact factor: 6.244

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

Authors:  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
Journal:  Ann Transl Med       Date:  2020-03

9.  Intravoxel incoherent motion diffusion-weighted MR imaging parameters predict pathological classification in thymic epithelial tumors.

Authors:  Gang-Feng Li; Shi-Jun Duan; Lin-Feng Yan; Wen Wang; Yong Jing; Wei-Qiang Yan; Qian Sun; Shu-Mei Wang; Hai-Yan Nan; Tian-Yong Xu; Dan-Dan Zheng; Yu-Chuan Hu; Guang-Bin Cui
Journal:  Oncotarget       Date:  2017-07-04

10.  Differentiating thymoma from thymic cyst in anterior mediastinal abnormalities smaller than 3 cm.

Authors:  Woohyun Jung; Sukki Cho; Sungwon Yum; Young Kyung Lee; Kwhanmien Kim; Sanghoon Jheon
Journal:  J Thorac Dis       Date:  2020-04       Impact factor: 2.895

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