Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels
Background Approximately 60% of Asian populations with non-small cell lung cancer (NSCLC) harbor epidermal growth factor receptor (EGFR) gene mutations, marking it as a pivotal target for genotype-directed therapies. Currently, determining EGFR mutation status relies on DNA sequencing of histologica...
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2024-12-01
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| author | Jimin Hao Man Liu Zhigang Zhou Chunling Zhao Liping Dai Songyun Ouyang |
| author_facet | Jimin Hao Man Liu Zhigang Zhou Chunling Zhao Liping Dai Songyun Ouyang |
| author_sort | Jimin Hao |
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| description | Background Approximately 60% of Asian populations with non-small cell lung cancer (NSCLC) harbor epidermal growth factor receptor (EGFR) gene mutations, marking it as a pivotal target for genotype-directed therapies. Currently, determining EGFR mutation status relies on DNA sequencing of histological or cytological specimens. This study presents a predictive model integrating clinical parameters, computed tomography (CT) characteristics, and serum tumor markers to forecast EGFR mutation status in NSCLC patients. Methods Retrospective data collection was conducted on NSCLC patients diagnosed between January 2018 and June 2019 at the First Affiliated Hospital of Zhengzhou University, with available molecular pathology results. Clinical information, CT imaging features, and serum tumor marker levels were compiled. Four distinct models were employed in constructing the diagnostic model. Model diagnostic efficacy was assessed through receiver operating characteristic (ROC) area under the curve (AUC) values and calibration curves. DeLong’s test was administered to validate model robustness. Results Our study encompassed 748 participants. Logistic regression modeling, trained with the aforementioned variables, demonstrated remarkable predictive capability, achieving an AUC of 0.805 (95% confidence interval (CI) [0.766–0.844]) in the primary cohort and 0.753 (95% CI [0.687–0.818]) in the validation cohort. Calibration plots suggested a favorable fit of the model to the data. Conclusions The developed logistic regression model emerges as a promising tool for forecasting EGFR mutation status. It holds potential to aid clinicians in more precisely identifying patients likely to benefit from EGFR molecular testing and facilitating targeted therapy decision-making, particularly in scenarios where molecular testing is impractical or inaccessible. |
| format | Article |
| id | doaj-art-45c884fca68143538ec10f3d7d79c52e |
| institution | Kabale University |
| issn | 2167-8359 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj-art-45c884fca68143538ec10f3d7d79c52e2024-12-05T15:05:18ZengPeerJ Inc.PeerJ2167-83592024-12-0112e1861810.7717/peerj.18618Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levelsJimin Hao0Man Liu1Zhigang Zhou2Chunling Zhao3Liping Dai4Songyun Ouyang5Department of Respiratory and Sleep Medicine, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, ChinaHenan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Radiology, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Respiratory and Sleep Medicine, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, ChinaHenan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Respiratory and Sleep Medicine, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, ChinaBackground Approximately 60% of Asian populations with non-small cell lung cancer (NSCLC) harbor epidermal growth factor receptor (EGFR) gene mutations, marking it as a pivotal target for genotype-directed therapies. Currently, determining EGFR mutation status relies on DNA sequencing of histological or cytological specimens. This study presents a predictive model integrating clinical parameters, computed tomography (CT) characteristics, and serum tumor markers to forecast EGFR mutation status in NSCLC patients. Methods Retrospective data collection was conducted on NSCLC patients diagnosed between January 2018 and June 2019 at the First Affiliated Hospital of Zhengzhou University, with available molecular pathology results. Clinical information, CT imaging features, and serum tumor marker levels were compiled. Four distinct models were employed in constructing the diagnostic model. Model diagnostic efficacy was assessed through receiver operating characteristic (ROC) area under the curve (AUC) values and calibration curves. DeLong’s test was administered to validate model robustness. Results Our study encompassed 748 participants. Logistic regression modeling, trained with the aforementioned variables, demonstrated remarkable predictive capability, achieving an AUC of 0.805 (95% confidence interval (CI) [0.766–0.844]) in the primary cohort and 0.753 (95% CI [0.687–0.818]) in the validation cohort. Calibration plots suggested a favorable fit of the model to the data. Conclusions The developed logistic regression model emerges as a promising tool for forecasting EGFR mutation status. It holds potential to aid clinicians in more precisely identifying patients likely to benefit from EGFR molecular testing and facilitating targeted therapy decision-making, particularly in scenarios where molecular testing is impractical or inaccessible.https://peerj.com/articles/18618.pdfNon-small cell lung cancer (NSCLC)Epidermal growth factor receptor (EGFR)Clinical characteristicsCT imaging featuresTumor marker levelsLogistic regression |
| spellingShingle | Jimin Hao Man Liu Zhigang Zhou Chunling Zhao Liping Dai Songyun Ouyang Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels PeerJ Non-small cell lung cancer (NSCLC) Epidermal growth factor receptor (EGFR) Clinical characteristics CT imaging features Tumor marker levels Logistic regression |
| title | Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels |
| title_full | Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels |
| title_fullStr | Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels |
| title_full_unstemmed | Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels |
| title_short | Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels |
| title_sort | predicting epidermal growth factor receptor egfr mutation status in non small cell lung cancer nsclc patients through logistic regression a model incorporating clinical characteristics computed tomography ct imaging features and tumor marker levels |
| topic | Non-small cell lung cancer (NSCLC) Epidermal growth factor receptor (EGFR) Clinical characteristics CT imaging features Tumor marker levels Logistic regression |
| url | https://peerj.com/articles/18618.pdf |
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