Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma
BackgroundTo investigate the value of computed tomography (CT)-based radiomics signatures in combination with clinical and CT morphological features to identify epidermal growth factor receptor (EGFR)-mutation subtypes in lung adenocarcinoma (LADC).MethodsFrom February 2012 to October 2019, 608 pati...
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| Format: | Article |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.846589/full |
| _version_ | 1811214577563598848 |
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| author | Ji-wen Huo Tian-you Luo Le Diao Fa-jin Lv Wei-dao Chen Rui-ze Yu Qi Li |
| author_facet | Ji-wen Huo Tian-you Luo Le Diao Fa-jin Lv Wei-dao Chen Rui-ze Yu Qi Li |
| author_sort | Ji-wen Huo |
| collection | DOAJ |
| description | BackgroundTo investigate the value of computed tomography (CT)-based radiomics signatures in combination with clinical and CT morphological features to identify epidermal growth factor receptor (EGFR)-mutation subtypes in lung adenocarcinoma (LADC).MethodsFrom February 2012 to October 2019, 608 patients were confirmed with LADC and underwent chest CT scans. Among them, 307 (50.5%) patients had a positive EGFR-mutation and 301 (49.5%) had a negative EGFR-mutation. Of the EGFR-mutant patients, 114 (37.1%) had a 19del -mutation, 155 (50.5%) had a L858R-mutation, and 38 (12.4%) had other rare mutations. Three combined models were generated by incorporating radiomics signatures, clinical, and CT morphological features to predict EGFR-mutation status. Patients were randomly split into training and testing cohorts, 80% and 20%, respectively. Model 1 was used to predict positive and negative EGFR-mutation, model 2 was used to predict 19del and non-19del mutations, and model 3 was used to predict L858R and non-L858R mutations. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate their performance.ResultsFor the three models, model 1 had AUC values of 0.969 and 0.886 in the training and validation cohorts, respectively. Model 2 had AUC values of 0.999 and 0.847 in the training and validation cohorts, respectively. Model 3 had AUC values of 0.984 and 0.806 in the training and validation cohorts, respectively.ConclusionCombined models that incorporate radiomics signature, clinical, and CT morphological features may serve as an auxiliary tool to predict EGFR-mutation subtypes and contribute to individualized treatment for patients with LADC. |
| first_indexed | 2024-04-12T06:05:49Z |
| format | Article |
| id | doaj.art-2eb9175e59864e85b797c58fcdcc12a0 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T06:05:49Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-2eb9175e59864e85b797c58fcdcc12a02022-12-22T03:44:51ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-08-011210.3389/fonc.2022.846589846589Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinomaJi-wen Huo0Tian-you Luo1Le Diao2Fa-jin Lv3Wei-dao Chen4Rui-ze Yu5Qi Li6Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaOcean International Center, The Infervision Medical Technology Co., Ltd., Beijing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaOcean International Center, The Infervision Medical Technology Co., Ltd., Beijing, ChinaOcean International Center, The Infervision Medical Technology Co., Ltd., Beijing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaBackgroundTo investigate the value of computed tomography (CT)-based radiomics signatures in combination with clinical and CT morphological features to identify epidermal growth factor receptor (EGFR)-mutation subtypes in lung adenocarcinoma (LADC).MethodsFrom February 2012 to October 2019, 608 patients were confirmed with LADC and underwent chest CT scans. Among them, 307 (50.5%) patients had a positive EGFR-mutation and 301 (49.5%) had a negative EGFR-mutation. Of the EGFR-mutant patients, 114 (37.1%) had a 19del -mutation, 155 (50.5%) had a L858R-mutation, and 38 (12.4%) had other rare mutations. Three combined models were generated by incorporating radiomics signatures, clinical, and CT morphological features to predict EGFR-mutation status. Patients were randomly split into training and testing cohorts, 80% and 20%, respectively. Model 1 was used to predict positive and negative EGFR-mutation, model 2 was used to predict 19del and non-19del mutations, and model 3 was used to predict L858R and non-L858R mutations. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate their performance.ResultsFor the three models, model 1 had AUC values of 0.969 and 0.886 in the training and validation cohorts, respectively. Model 2 had AUC values of 0.999 and 0.847 in the training and validation cohorts, respectively. Model 3 had AUC values of 0.984 and 0.806 in the training and validation cohorts, respectively.ConclusionCombined models that incorporate radiomics signature, clinical, and CT morphological features may serve as an auxiliary tool to predict EGFR-mutation subtypes and contribute to individualized treatment for patients with LADC.https://www.frontiersin.org/articles/10.3389/fonc.2022.846589/fulllung cancerepidermal growth factor receptorradiomicscomputed tomographymachine learning |
| spellingShingle | Ji-wen Huo Tian-you Luo Le Diao Fa-jin Lv Wei-dao Chen Rui-ze Yu Qi Li Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma Frontiers in Oncology lung cancer epidermal growth factor receptor radiomics computed tomography machine learning |
| title | Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma |
| title_full | Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma |
| title_fullStr | Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma |
| title_full_unstemmed | Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma |
| title_short | Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma |
| title_sort | using combined ct clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma |
| topic | lung cancer epidermal growth factor receptor radiomics computed tomography machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.846589/full |
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