Wang Xun,Ge Shuang,Xi Huizhen,Ma Jun,Liu Yaru,Ye Shucheng,Ma Junli.Prediction of EGFR mutation status in lung adenocarcinoma based on standardized enhanced CT radiomics nomogram[J].Chinese Journal of Radiological Medicine and Protection,2024,44(3):194-201
Prediction of EGFR mutation status in lung adenocarcinoma based on standardized enhanced CT radiomics nomogram
Received:August 23, 2023  
DOI:10.3760/cma.j.cn112271-20230823-00059
KeyWords:Lung adenocarcinoma  Epidermal growth factor receptor  Radiomics  Predition  Mutation
FundProject:济宁市重点研发计划项目(2022YXNS029)
Author NameAffiliationE-mail
Wang Xun Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining 272000, China  
Ge Shuang Department of Oncology Radiotherapy, Affiliated Hospital of Jining Medical University, Jining 272000, China  
Xi Huizhen Department of Oncology Radiotherapy, Affiliated Hospital of Jining Medical University, Jining 272000, China  
Ma Jun Department of Oncology Radiotherapy, Affiliated Hospital of Jining Medical University, Jining 272000, China  
Liu Yaru Department of Clinical Medicine, Jining Medical University, Jining 272000, China  
Ye Shucheng Department of Oncology Radiotherapy, Affiliated Hospital of Jining Medical University, Jining 272000, China  
Ma Junli Department of Oncology Radiotherapy, Affiliated Hospital of Jining Medical University, Jining 272000, China majl2015@yeah.net 
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Abstract::
      Objective To investigate the value of radiomics nomogram based on standardized pre-treatment chest enhanced CT in predicting the mutation status of epidermal growth factor receptor (EGFR) for patients with lung adenocarcinoma. Methods A retrospective analysis was conducted on pre-treatment chest enhanced CT images and clinical data of 262 patients from the affiliated hospital of Jining Medical University with pathologically proven primary lung adenocarcinoma who received EGFR gene testing, including EGFR wild type (n=122) and mutant type (n=140). The patients were divided into training group (n=183) and testing group (n=79) according to a ratio of 7∶3 by stratified sampling method. Standardized pre-processed the images, delineated the ROI and extracted the radiomics features. Least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension and select key features. The standardized radiomics model, clinical model and the combined model were established by Logistic Regression (LR) machine learning method. Calculated the Rad-score and drew the nomogram. ROC curve and Delong were used to evaluate and compare the predictive performance of different models. Results 23 standardized enhanced CT radiomics features and 4 clinical features were selected. The predictive performance of standardized radiomics model was better than that of non-standardized radiomics model [area under curve (AUC):0.863 vs. 0.805,t=2.19,P<0.05]. The AUCs of the combined model and standardized radiomics model were higher than that of the clinical model (training group:0.885,0.863 vs. 0.774,t=3.57,2.17,P<0.05;testing group: 0.873,0.829 vs. 0.763,t=2.19,2.02, P<0.05). The radiomics nomogram was built based on Rad-score,age,sex, smoking history and BMI. Conclusions The combined model and standardized radiomics model could effectively predict the mutation status of EGFR gene in lung adenocarcinoma patients before treatment, providing valuable clinical insights.
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