Literature DB >> 25534526

Thymic epithelial tumors: prognostic determinants among clinical, histopathologic, and computed tomography findings.

Jung Won Moon1, Kyung Soo Lee2, Myung-Hee Shin3, Seonwoo Kim4, Sook Young Woo4, Geewon Lee5, Joungho Han6, Young Mog Shim7, Yong Soo Choi7.   

Abstract

BACKGROUND: The Masaoka-Koga staging system has been known as the strongest prognostic factor for both survival and recurrence of thymic epithelial tumor (TET). The purpose of our study was to find prognostic determinants among computed tomography (CT), histopathologic, and clinical features of TET.
METHODS: Two radiologists reviewed retrospectively CT findings of 437 patients (male 242, female 195; mean age, 51 years) with TET. With medical record review, surgico-histopathologic results were subcategorized into Masaoka-Koga stages I through IV and World Health Organization histopathologic classifications A-B1, B2-B3, and carcinoma. Overall survival and progression-free survival were analyzed. Clinical, histopathologic, and CT features were correlated from each other.
RESULTS: In all, 437 tumors were in Masaoka-Koga stage I (n = 147, 33.6%), stage II (n = 121, 27.7%), stage III (n = 76, 17.4%), or stage IV (n = 93, 21.3%); A and B1 (n = 114, 26.1%) and B2 and B3 TET (n = 223, 51.0%); and thymic carcinoma (n = 100, 22.9%). In multivariable analyses, age, Masaoka-Koga stage IV, thymic carcinoma, and CT stages III and IV were significantly correlated with overall survival (p < 0.05), whereas adjuvant treatment, Masaoka-Koga stages III and IV, World Health Organization B2 and B3, thymic carcinoma, R2 resection, CT size, and CT stage IV were significantly associated with progression-free survival (p < 0.05). Computed tomography stages showed moderate association with Masaoka-Koga stages (K = 0.621).
CONCLUSIONS: For TET, CT staging is effective in distinguishing both overall survival and progression-free survival, and patients with Masaoka-Koga stage IV or thymic carcinoma or CT stage IV have the worst prognosis.
Copyright © 2015 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

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Year:  2014        PMID: 25534526     DOI: 10.1016/j.athoracsur.2014.09.050

Source DB:  PubMed          Journal:  Ann Thorac Surg        ISSN: 0003-4975            Impact factor:   4.330


  13 in total

Review 1.  Paraneoplastic and Therapy-Related Immune Complications in Thymic Malignancies.

Authors:  Elizabeth A Lippner; David B Lewis; William H Robinson; Tamiko R Katsumoto
Journal:  Curr Treat Options Oncol       Date:  2019-06-22

Review 2.  Thymic tumors and immune checkpoint inhibitors.

Authors:  Shintaro Yokoyama; Hiroaki Miyoshi
Journal:  J Thorac Dis       Date:  2018-05       Impact factor: 2.895

Review 3.  Robotic thymectomy for advanced thymic epithelial tumor: indications and technical aspects.

Authors:  Kwon Joong Na; Chang Hyun Kang
Journal:  J Thorac Dis       Date:  2020-02       Impact factor: 2.895

4.  Radiographic Predictors of Resectability in Thymic Carcinoma.

Authors:  Sara A Hayes; James Huang; Jennifer Golia Pernicka; Jane Cunningham; Junting Zheng; Chaya S Moskowitz; Michelle S Ginsberg
Journal:  Ann Thorac Surg       Date:  2018-03-11       Impact factor: 4.330

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.  Carbonic anhydrase 9 expression is associated with poor prognosis, tumor proliferation, and radiosensitivity of thymic carcinomas.

Authors:  Yoichi Ohtaki; Kimihiro Shimizu; Reika Kawabata-Iwakawa; Navchaa Gombodorj; Bolag Altan; Susumu Rokudai; Arito Yamane; Kyoichi Kaira; Takehiko Yokobori; Toshiteru Nagashima; Kai Obayashi; Seshiru Nakazawa; Misaki Iijima; Takayuki Kosaka; Toshiki Yajima; Akira Mogi; Hiroyuki Kuwano; Ken Shirabe; Masahiko Nishiyama
Journal:  Oncotarget       Date:  2019-02-12

7.  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

8.  PD-L1 Expression and Tumor-Infiltrating Lymphocytes in Thymic Epithelial Neoplasms.

Authors:  Rumi Higuchi; Taichiro Goto; Yosuke Hirotsu; Takahiro Nakagomi; Yujiro Yokoyama; Sotaro Otake; Kenji Amemiya; Toshio Oyama; Masao Omata
Journal:  J Clin Med       Date:  2019-11-01       Impact factor: 4.241

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.  Dual-energy CT perfusion imaging for differentiating WHO subtypes of thymic epithelial tumors.

Authors:  Chunhai Yu; Ting Li; Ruiping Zhang; Xiaotang Yang; Zhao Yang; Lei Xin; Zhikai Zhao
Journal:  Sci Rep       Date:  2020-03-26       Impact factor: 4.379

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