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CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma
OBJECTIVE: To identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma. MATERIALS AND ME...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556025/ https://www.ncbi.nlm.nih.gov/pubmed/31174617 http://dx.doi.org/10.1186/s40644-019-0221-9 |
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author | Shen, Tong-xu Liu, Lin Li, Wen-hui Fu, Ping Xu, Kai Jiang, Yu-qing Pan, Feng Guo, Yan Zhang, Meng-chao |
author_facet | Shen, Tong-xu Liu, Lin Li, Wen-hui Fu, Ping Xu, Kai Jiang, Yu-qing Pan, Feng Guo, Yan Zhang, Meng-chao |
author_sort | Shen, Tong-xu |
collection | PubMed |
description | OBJECTIVE: To identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma. MATERIALS AND METHODS: This retrospective study included 57 patients, with pathologically confirmed bone metastasis of primary lung adenocarcinoma. EGFR mutation status of bone metastases was confirmed by gene detection. The CT imaging of the metastatic bone lesions which were obtained between June 2014 and December 2017 were collected and analyzed. A total of 42 CT imaging-based histogram features were automatically extracted. Feature selection was conducted using Student’s t-test, Mann-Whitney U test, single-factor logistic regression analysis and Spearman correlation analysis. A receiver operating characteristic (ROC) curve was plotted to compare the effectiveness of features in distinguishing between EGFR(+) and EGFR(−) groups. DeLong’s test was used to analyze the differences between the area under the curve (AUC) values. RESULTS: Three histogram features, namely range, skewness, and quantile 0.975 were significantly associated with EGFR mutation status. After combining these three features and combining range and skewness, we obtained the same AUC values, sensitivity and specificity. Meanwhile, the highest AUC value was achieved (AUC 0.783), which also had a higher sensitivity (0.708) and specificity (0.788). The differences between AUC values of the three features and their various combinations were statistically insignificant. CONCLUSION: CT imaging-based histogram features of bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma were identified, and they may contribute to diagnosis and prediction of EGFR mutation status. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40644-019-0221-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6556025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65560252019-06-13 CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma Shen, Tong-xu Liu, Lin Li, Wen-hui Fu, Ping Xu, Kai Jiang, Yu-qing Pan, Feng Guo, Yan Zhang, Meng-chao Cancer Imaging Research Article OBJECTIVE: To identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma. MATERIALS AND METHODS: This retrospective study included 57 patients, with pathologically confirmed bone metastasis of primary lung adenocarcinoma. EGFR mutation status of bone metastases was confirmed by gene detection. The CT imaging of the metastatic bone lesions which were obtained between June 2014 and December 2017 were collected and analyzed. A total of 42 CT imaging-based histogram features were automatically extracted. Feature selection was conducted using Student’s t-test, Mann-Whitney U test, single-factor logistic regression analysis and Spearman correlation analysis. A receiver operating characteristic (ROC) curve was plotted to compare the effectiveness of features in distinguishing between EGFR(+) and EGFR(−) groups. DeLong’s test was used to analyze the differences between the area under the curve (AUC) values. RESULTS: Three histogram features, namely range, skewness, and quantile 0.975 were significantly associated with EGFR mutation status. After combining these three features and combining range and skewness, we obtained the same AUC values, sensitivity and specificity. Meanwhile, the highest AUC value was achieved (AUC 0.783), which also had a higher sensitivity (0.708) and specificity (0.788). The differences between AUC values of the three features and their various combinations were statistically insignificant. CONCLUSION: CT imaging-based histogram features of bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma were identified, and they may contribute to diagnosis and prediction of EGFR mutation status. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40644-019-0221-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-07 /pmc/articles/PMC6556025/ /pubmed/31174617 http://dx.doi.org/10.1186/s40644-019-0221-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Shen, Tong-xu Liu, Lin Li, Wen-hui Fu, Ping Xu, Kai Jiang, Yu-qing Pan, Feng Guo, Yan Zhang, Meng-chao CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma |
title | CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma |
title_full | CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma |
title_fullStr | CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma |
title_full_unstemmed | CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma |
title_short | CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma |
title_sort | ct imaging-based histogram features for prediction of egfr mutation status of bone metastases in patients with primary lung adenocarcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556025/ https://www.ncbi.nlm.nih.gov/pubmed/31174617 http://dx.doi.org/10.1186/s40644-019-0221-9 |
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