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Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma
OBJECTIVE: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on (18)F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Society of Radiology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9434738/ https://www.ncbi.nlm.nih.gov/pubmed/36047542 http://dx.doi.org/10.3348/kjr.2022.0295 |
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author | Li, Shen Li, Yadi Zhao, Min Wang, Pengyuan Xin, Jun |
author_facet | Li, Shen Li, Yadi Zhao, Min Wang, Pengyuan Xin, Jun |
author_sort | Li, Shen |
collection | PubMed |
description | OBJECTIVE: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on (18)F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. MATERIALS AND METHODS: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. RESULTS: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. CONCLUSION: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics. |
format | Online Article Text |
id | pubmed-9434738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Korean Society of Radiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-94347382022-09-07 Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma Li, Shen Li, Yadi Zhao, Min Wang, Pengyuan Xin, Jun Korean J Radiol Thoracic Imaging OBJECTIVE: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on (18)F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. MATERIALS AND METHODS: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. RESULTS: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. CONCLUSION: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics. The Korean Society of Radiology 2022-09 2022-08-09 /pmc/articles/PMC9434738/ /pubmed/36047542 http://dx.doi.org/10.3348/kjr.2022.0295 Text en Copyright © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Thoracic Imaging Li, Shen Li, Yadi Zhao, Min Wang, Pengyuan Xin, Jun Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma |
title | Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma |
title_full | Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma |
title_fullStr | Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma |
title_full_unstemmed | Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma |
title_short | Combination of (18)F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma |
title_sort | combination of (18)f-fluorodeoxyglucose pet/ct radiomics and clinical features for predicting epidermal growth factor receptor mutations in lung adenocarcinoma |
topic | Thoracic Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9434738/ https://www.ncbi.nlm.nih.gov/pubmed/36047542 http://dx.doi.org/10.3348/kjr.2022.0295 |
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