Bibliographic Details
| 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 |
| Authors: |
Jimin Hao, Man Liu, Zhigang Zhou, Chunling Zhao, Liping Dai, Songyun Ouyang |
| Source: |
PeerJ, Vol 12, p e18618 (2024) |
| Publisher Information: |
PeerJ Inc., 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Medicine
LCC:Biology (General) |
| Subject Terms: |
Non-small cell lung cancer (NSCLC), Epidermal growth factor receptor (EGFR), Clinical characteristics, CT imaging features, Tumor marker levels, Logistic regression, Medicine, Biology (General), QH301-705.5 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2167-8359 |
| Relation: |
https://peerj.com/articles/18618.pdf; https://peerj.com/articles/18618/; https://doaj.org/toc/2167-8359 |
| DOI: |
10.7717/peerj.18618 |
| Access URL: |
https://doaj.org/article/45c884fca68143538ec10f3d7d79c52e |
| Accession Number: |
edsdoj.45c884fca68143538ec10f3d7d79c52e |
| Database: |
Directory of Open Access Journals |
