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(18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma

BACKGROUND: To investigate whether radiomic features from ((18)F)-fluorodeoxyglucose positron emission tomography/computed tomography [((18)F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma. METHODS: One hundred and...

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Autores principales: Yang, Bin, Ji, Heng-Shan, Zhou, Chang-Sheng, Dong, Hao, Ma, Lu, Ge, Ying-Qian, Zhu, Chao-Hui, Tian, Jia-He, Zhang, Long-Jiang, Zhu, Hong, Lu, Guang-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354130/
https://www.ncbi.nlm.nih.gov/pubmed/32676320
http://dx.doi.org/10.21037/tlcr-19-592
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author Yang, Bin
Ji, Heng-Shan
Zhou, Chang-Sheng
Dong, Hao
Ma, Lu
Ge, Ying-Qian
Zhu, Chao-Hui
Tian, Jia-He
Zhang, Long-Jiang
Zhu, Hong
Lu, Guang-Ming
author_facet Yang, Bin
Ji, Heng-Shan
Zhou, Chang-Sheng
Dong, Hao
Ma, Lu
Ge, Ying-Qian
Zhu, Chao-Hui
Tian, Jia-He
Zhang, Long-Jiang
Zhu, Hong
Lu, Guang-Ming
author_sort Yang, Bin
collection PubMed
description BACKGROUND: To investigate whether radiomic features from ((18)F)-fluorodeoxyglucose positron emission tomography/computed tomography [((18)F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma. METHODS: One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent ((18)F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model’s performance. RESULTS: Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03). CONCLUSIONS: Radiomic features based on ((18)F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis.
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spelling pubmed-73541302020-07-15 (18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma Yang, Bin Ji, Heng-Shan Zhou, Chang-Sheng Dong, Hao Ma, Lu Ge, Ying-Qian Zhu, Chao-Hui Tian, Jia-He Zhang, Long-Jiang Zhu, Hong Lu, Guang-Ming Transl Lung Cancer Res Original Article BACKGROUND: To investigate whether radiomic features from ((18)F)-fluorodeoxyglucose positron emission tomography/computed tomography [((18)F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma. METHODS: One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent ((18)F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model’s performance. RESULTS: Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03). CONCLUSIONS: Radiomic features based on ((18)F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis. AME Publishing Company 2020-06 /pmc/articles/PMC7354130/ /pubmed/32676320 http://dx.doi.org/10.21037/tlcr-19-592 Text en 2020 Translational Lung Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Yang, Bin
Ji, Heng-Shan
Zhou, Chang-Sheng
Dong, Hao
Ma, Lu
Ge, Ying-Qian
Zhu, Chao-Hui
Tian, Jia-He
Zhang, Long-Jiang
Zhu, Hong
Lu, Guang-Ming
(18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma
title (18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma
title_full (18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma
title_fullStr (18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma
title_full_unstemmed (18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma
title_short (18)F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma
title_sort (18)f-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354130/
https://www.ncbi.nlm.nih.gov/pubmed/32676320
http://dx.doi.org/10.21037/tlcr-19-592
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