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Updated: Sep 27, 2025
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Establishment and Evaluation of EGFR Mutation Prediction Model Based on Tumor Markers and CT Features in NSCLC
Hao Zhang1, Meng He1, Ren'an Wan1
1Department of Thoracic Surgery, Rizhao People's Hospital, Rizhao 276800, Shandong, China.
Background:
Lung cancer has become one of the leading causes of cancer deaths worldwide. EGFR gene mutation has been reported in up to 60% of Asian populations and is currently one of the main targets for genotype-targeted therapy for NSCLC.
Objective:
The objective is to determine if a complex model combining serum tumor makers and computed tomographic (CT) features can predict epidermal growth factor receptor (EGFR) mutation with higher accuracy. Material and Methods. Retrospective analysis of the data of patients diagnosed with in nonsmall cell lung cancer (NSCLC) by EGFR gene testing was carried out in the Department of Thoracic Surgery, Jinan Central Hospital. Multivariate logistic regression analysis was used to determine the independent predictors of EGFR mutations, and logistic regression prediction models were developed. The subject operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to assess the accuracy and clinical application of the EGFR mutation prediction model.
Results:
Logistic regression analysis identified the predictive factors of EGFR mutation including nonsmoking, high expression level of Carcinoembryonic Antigen (CEA), low expression level of cytokeratin 19 fragments (CYFRA21-1), and subsolid density containing ground-glass opacity (GGO) component. Using the results of multivariate logistic regression analysis, we built a statistically determined clinical prediction model. The AUC of the complex prediction model increased significantly from 0.735 to 0.813 (p = 0.014) when CT features are added and from 0.612 to 0.813 (p < 0.001) when serum variables are added. When P was 0.441, the sensitivity was 86.7% and the specificity was 65.8%.
Conclusion:
A complex model combining serum tumor makers and CT features is more accurate in predicting EGFR mutation status in NSCLC patients than using either serum variables or imaging features alone. Our finding for EGFR mutation is urgently needed and helpful in clinical practice.
