Literature DB >> 24568474

Relationships between EGFR mutation status of lung cancer and preoperative factors - are they predictive?

Katsuo Usuda1, Motoyasu Sagawa, Nozomu Motono, Masakatsu Ueno, Makoto Tanaka, Yuichiro Machida, Munetaka Matoba, Mitsuru Taniguchi, Hisao Tonami, Yoshimichi Ueda, Tsutomu Sakuma.   

Abstract

BACKGROUND: The epidermal growth factor receptor (EGFR) mutation status of lung cancer is important because it means that EGFR-tyrosine kinase inhibitor treatment is indicated. The purpose of this prospective study is to determine whether EGFR mutation status could be identified with reference to preoperative factors.
MATERIALS AND METHODS: One hundred-forty eight patients with lung cancer (111 adenocarcinomas, 25 squamous cell carcinomas and 12 other cell types) were enrolled in this study. The EGFR mutation status of each lung cancer was analyzed postoperatively.
RESULTS: There were 58 patients with mutant EGFR lung cancers (mutant LC) and 90 patients with wild-type EGFR lung cancers (wild-type LC). There were significant differences in gender, smoking status, maximum tumor diameter in chest CT, type of tumor shadow, clinical stage between mutant LC and wild-type LC. EGFR mutations were detected only in adenocarcinomas. Maximum standardized uptake value (SUVmax:3.66±4.53) in positron emission tomography-computed tomography of mutant LC was significantly lower than that (8.26±6.11) of wild-type LC (p<0.0001). Concerning type of tumor shadow, the percentage of mutant LC was 85.7% (6/7) in lung cancers with pure ground glass opacity (GGO), 65.3%(32/49) in lung cancers with mixed GGO and 21.7%(20/92) in lung cancers with solid shadow (p<0.0001). For the results of discriminant analysis, type of tumor shadow (p=0.00036) was most significantly associated with mutant EGFR. Tumor histology (p=0.0028), smoking status (p=0.0051) and maximum diameter of tumor shadow in chest CT (p=0.047) were also significantly associated with mutant EGFR. The accuracy for evaluating EGFR mutation status by discriminant analysis was 77.0% (114/148).
CONCLUSIONS: Mutant EGFR is significantly associated with lung cancer with pure or mixed GGO, adenocarcinoma, never-smoker, smaller tumor diameter in chest CT. Preoperatively, EGFR mutation status can be identified correctly in about 77 % of lung cancers.

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Year:  2014        PMID: 24568474     DOI: 10.7314/apjcp.2014.15.2.657

Source DB:  PubMed          Journal:  Asian Pac J Cancer Prev        ISSN: 1513-7368


  21 in total

Review 1.  Pulmonary ground-glass opacity: computed tomography features, histopathology and molecular pathology.

Authors:  Jian-Wei Gao; Stefania Rizzo; Li-Hong Ma; Xiang-Yu Qiu; Arne Warth; Nobuhiko Seki; Mizue Hasegawa; Jia-Wei Zou; Qian Li; Marco Femia; Tang-Feng Lv; Yong Song
Journal:  Transl Lung Cancer Res       Date:  2017-02

2.  Significance of the epidermal growth factor receptor mutation status and differences among molecular subgroups in surgically resected lung microinvasive adenocarcinoma.

Authors:  Ming Li; Chuanying Li; Li Ke; Mali Zhan; Min Cheng
Journal:  Oncol Lett       Date:  2018-10-02       Impact factor: 2.967

3.  Genomic landscape of ground glass opacities (GGOs) in East Asians.

Authors:  Peng Cao; Shan Hu; Kangle Kong; Peng Han; Jiaqi Yue; Yu Deng; Bo Zhao; Fan Li
Journal:  J Thorac Dis       Date:  2021-04       Impact factor: 2.895

4.  The Association of EGFR Mutations with Stage at Diagnosis in Lung Adenocarcinomas.

Authors:  Jaeyoung Cho; Sun Mi Choi; Jinwoo Lee; Chang-Hoon Lee; Sang-Min Lee; Jae-Joon Yim; Doo Hyun Chung; Chul-Gyu Yoo; Young Whan Kim; Sung Koo Han; Young Sik Park
Journal:  PLoS One       Date:  2016-11-18       Impact factor: 3.240

5.  A novel ARMS-based assay for the quantification of EGFR mutations in patients with lung adenocarcinoma.

Authors:  Yazhen Zhu; Zhiwei Guo; Ying Liu; Xiyun Zheng; Guohua Yang; Guangjuan Zheng
Journal:  Oncol Lett       Date:  2017-12-21       Impact factor: 2.967

6.  Relationship of EGFR Mutation to Glucose Metabolic Activity and Asphericity of Metabolic Tumor Volume in Lung Adenocarcinoma.

Authors:  Wonseok Whi; Seunggyun Ha; Sungwoo Bae; Hongyoon Choi; Jin Chul Paeng; Gi Jeong Cheon; Keon Wook Kang; Dong Soo Lee
Journal:  Nucl Med Mol Imaging       Date:  2020-06-14

7.  Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images.

Authors:  Yunyun Dong; Lina Hou; Wenkai Yang; Jiahao Han; Jiawen Wang; Yan Qiang; Juanjuan Zhao; Jiaxin Hou; Kai Song; Yulan Ma; Ntikurako Guy Fernand Kazihise; Yanfen Cui; Xiaotang Yang
Journal:  Quant Imaging Med Surg       Date:  2021-06

8.  Association between histopathological subtype, 18F-fluorodeoxyglucose uptake and epidermal growth factor receptor mutations in lung adenocarcinoma.

Authors:  Guangliang Qiang; Wei Huang; Chaoyang Liang; Rui Xu; Jue Yan; Yanyan Xu; Y E Wang; Jiping DA; Bin Shi; Yongqing Guo; Deruo Liu
Journal:  Oncol Lett       Date:  2016-01-27       Impact factor: 2.967

9.  Associations between clinical data and computed tomography features in patients with epidermal growth factor receptor mutations in lung adenocarcinoma.

Authors:  Yiyuan Cao; Haibo Xu; Meiyan Liao; Yanjuan Qu; Liying Xu; Dongyong Zhu; Bicheng Wang; Sufang Tian
Journal:  Int J Clin Oncol       Date:  2017-10-07       Impact factor: 3.402

10.  Correlations Between the EGFR Mutation Status and Clinicopathological Features of Clinical Stage I Lung Adenocarcinoma.

Authors:  Tetsuya Isaka; Tomoyuki Yokose; Hiroyuki Ito; Masashi Nagata; Hideyuki Furumoto; Teppei Nishii; Kayoko Katayama; Kouzo Yamada; Haruhiko Nakayama; Munetaka Masuda
Journal:  Medicine (Baltimore)       Date:  2015-10       Impact factor: 1.817

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