Literature DB >> 26496549

CT Gray-Level Texture Analysis as a Quantitative Imaging Biomarker of Epidermal Growth Factor Receptor Mutation Status in Adenocarcinoma of the Lung.

Efe Ozkan1, Anna West1, Jeffrey A Dedelow1, Benjamin F Chu2, Weiqiang Zhao3, Vedat O Yildiz4, Gregory A Otterson5, Konstantin Shilo3, Subha Ghosh1, Mark King1, Richard D White1, Barbaros S Erdal1.   

Abstract

OBJECTIVE: The purpose of this study was to investigate the radiogenomic correlation between CT gray-level texture features and epidermal growth factor receptor (EGFR) mutation status in adenocarcinoma of the lung.
MATERIALS AND METHODS: This retrospective study included 25 patients with exon 19 short inframe deletion (exon 19) and 21 patients with exon 21 L858R point (exon 21) EGFR mutations among 125 patients with EGFR mutant adenocarcinoma of the lung. The randomly formed control group consisted of 20 patients selected from 126 patients with EGFR mutation-negative (wild-type) adenocarcinomas. Five gray-level texture features (contrast, correlation, inverse difference moment, angular second moment, and entropy) were analyzed.
RESULTS: Contrast differentiated both exon 19 (p = 0.00027) and exon 21 (p = 0.00001) mutants from the wild type. Wild-type adenocarcinomas had high scores for contrast (mean, 1598.547) compared with EGFR mutants (mean, 679.463). Correlation differentiated both exon 19 (p = 0.017) and exon 21 (p = 0.0015) mutants from wild-type adenocarcinomas. Inverse difference moment differentiated exon 19 mutants from exon 21 mutants (p = 0.019) and both exon 19 (p = 0.044) and exon 21 (p = 0.00001) mutants from wild-type adenocarcinomas. Angular second moment and entropy were not associated with statistically significant differences between mutation statuses.
CONCLUSION: Contrast, correlation, and inverse difference moment texture features correlate with EGFR mutation status in adenocarcinoma of the lung. Further investigation with larger prospective studies is needed to validate the role of CT gray-level texture analysis as a quantitative imaging biomarker.

Entities:  

Keywords:  CT; adenocarcinoma of the lung; biomarker; epidermal growth factor receptor mutation; quantitative imaging

Mesh:

Substances:

Year:  2015        PMID: 26496549     DOI: 10.2214/AJR.14.14147

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  27 in total

1.  Radiology and Enterprise Medical Imaging Extensions (REMIX).

Authors:  Barbaros S Erdal; Luciano M Prevedello; Songyue Qian; Mutlu Demirer; Kevin Little; John Ryu; Thomas O'Donnell; Richard D White
Journal:  J Digit Imaging       Date:  2018-02       Impact factor: 4.056

2.  Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.

Authors:  Ying Liu; Jongphil Kim; Yoganand Balagurunathan; Qian Li; Alberto L Garcia; Olya Stringfield; Zhaoxiang Ye; Robert J Gillies
Journal:  Clin Lung Cancer       Date:  2016-02-16       Impact factor: 4.785

Review 3.  Imaging genomics in cancer research: limitations and promises.

Authors:  Harrison X Bai; Ashley M Lee; Li Yang; Paul Zhang; Christos Davatzikos; John M Maris; Sharon J Diskin
Journal:  Br J Radiol       Date:  2016-02-11       Impact factor: 3.039

4.  Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer.

Authors:  Jianyuan Zhang; Xinming Zhao; Yan Zhao; Jingmian Zhang; Zhaoqi Zhang; Jianfang Wang; Yingchen Wang; Meng Dai; Jingya Han
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-11-14       Impact factor: 9.236

5.  Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature.

Authors:  Xiaobo Zhang; Bingfeng Lu; Xinguan Yang; Dong Lan; Shushen Lin; Zhipeng Zhou; Kai Li; Dong Deng; Peng Peng; Zisan Zeng; Liling Long
Journal:  Eur Radiol       Date:  2022-09-27       Impact factor: 7.034

Review 6.  Radiomics in immuno-oncology.

Authors:  Z Bodalal; I Wamelink; S Trebeschi; R G H Beets-Tan
Journal:  Immunooncol Technol       Date:  2021-04-16

Review 7.  Clinical applications of textural analysis in non-small cell lung cancer.

Authors:  Iain Phillips; Mazhar Ajaz; Veni Ezhil; Vineet Prakash; Sheaka Alobaidli; Sarah J McQuaid; Christopher South; James Scuffham; Andrew Nisbet; Philip Evans
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

8.  CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts.

Authors:  Su Joa Ahn; Jung Hoon Kim; Sang Min Lee; Sang Joon Park; Joon Koo Han
Journal:  Eur Radiol       Date:  2018-11-19       Impact factor: 5.315

9.  A new predictive scoring system based on clinical data and computed tomography features for diagnosing EGFR-mutated lung adenocarcinoma.

Authors:  Y Cao; H Xu
Journal:  Curr Oncol       Date:  2018-04-30       Impact factor: 3.677

10.  Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

Authors:  Laurent Dercle; Matthew Fronheiser; Lin Lu; Shuyan Du; Wendy Hayes; David K Leung; Amit Roy; Julia Wilkerson; Pingzhen Guo; Antonio T Fojo; Lawrence H Schwartz; Binsheng Zhao
Journal:  Clin Cancer Res       Date:  2020-03-20       Impact factor: 13.801

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