Literature DB >> 30777218

Prediction of EGFR mutations by conventional CT-features in advanced pulmonary adenocarcinoma.

Yanqing Chen1, Yang Yang2, Longbai Ma1, Huiyuan Zhu3, Tienan Feng4, Sen Jiang2, Youyong Wei1, Tingting Wang2, Xiwen Sun5.   

Abstract

OBJECTIVE: This study assessed the ability of conventional computed tomography (CT) features (including primary tumors, metastatic lesions, lymph nodes, and emphysema) to predict epidermal growth factor receptor (EGFR) mutations in advanced pulmonary adenocarcinoma.
METHODS: Patients who were diagnosed with advanced pulmonary adenocarcinoma between January 2017 and August 2017 and had undergone a chest CT and EGFR mutation testing were enrolled in this retrospective study. Qualitative and quantitative CT-features and clinical characteristics evaluated in this study included: primary tumor location, size, and morphology (including degree of lobulation, density, calcification, cavitation, vacuole sign, and air bronchogram), size and distribution of lung and pleural metastatic nodules, size and status of hilar and mediastinal lymph nodes, emphysema, organs with distant metastasis, and patient age, sex, and smoking history.
RESULTS: Of 201 patients, 107 (53.23%) were EGFR-mutation positive. The multivariate logistic regression indicated that EGFR mutations were significantly associated with smaller lymph nodes, a lower percentage of deep lobulation of the primary tumor and partial fusion of lymph nodes, and absence of emphysema. The area under the curve of logistic regression model for predicting EGFR mutations was 0.898.
CONCLUSIONS: Conventional CT-features, including emphysema, degree of primary tumor lobulation, and lymph node size and status, help to predict the presence or absence of EGFR mutations in advanced pulmonary adenocarcinoma. Additionally, these same CT-features demonstrated that the CT manifestations of the EGFR mutant group were of relatively lower malignancy and less invasive as compared to the wild-type EGFR group.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Advanced pulmonary adenocarcinoma; Computed tomography (CT); Epidermal growth factor receptor (EGFR) mutations

Mesh:

Substances:

Year:  2019        PMID: 30777218     DOI: 10.1016/j.ejrad.2019.01.005

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  4 in total

1.  Establishment and Evaluation of EGFR Mutation Prediction Model Based on Tumor Markers and CT Features in NSCLC.

Authors:  Hao Zhang; Meng He; Ren'an Wan; Liangming Zhu; Xiangpeng Chu
Journal:  J Healthc Eng       Date:  2022-04-05       Impact factor: 2.682

2.  Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis.

Authors:  Andrés Felipe Herrera Ortiz; Tatiana Cadavid Camacho; Andrés Francisco Vásquez; Valeria Del Castillo Herazo; Juan Guillermo Arámbula Neira; María Mónica Yepes; Eduard Cadavid Camacho
Journal:  Eur J Radiol Open       Date:  2022-02-07

3.  Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.

Authors:  Yuze Wang; Qi Wan; Xiaoying Xia; Jianfeng Hu; Yuting Liao; Peng Wang; Yu Peng; Hongyan Liu; Xinchun Li
Journal:  J Thorac Dis       Date:  2021-06       Impact factor: 2.895

4.  Computed Tomography Morphological Classification of Lung Adenocarcinoma and Its Correlation with Epidermal Growth Factor Receptor Mutation Status: A Report of 1075 Cases.

Authors:  Xiao-Qun He; Xing-Tao Huang; Qi Li; Xiao Fan; Tian-You Luo; Ji-Wen Huo
Journal:  Int J Gen Med       Date:  2021-07-21
  4 in total

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