Prediction of EGFR mutant subtypes in patients with non-small cell lung cancer by pre-treatment CT radiomics and machine learning
10.3760/cma.j.cn112271-20221223-00495
- VernacularTitle:治疗前CT影像组学结合机器学习预测非小细胞肺癌患者EGFR突变亚型
- Author:
Jiang HU
1
;
Ruimin HE
;
Pinjing CHENG
;
Xiaomin LIU
;
Haibiao WU
;
Linfei LIU
;
Baiqi WANG
;
Hao CHENG
;
Junhui YANG
Author Information
1. 南华大学核科学技术学院，衡阳 421001
- Keywords:
Non-small cell lung cancer;
Epidermal growth factor receptor;
Computed tomography;
Radiomics;
Machine learning
- From:
Chinese Journal of Radiological Medicine and Protection
2023;43(5):386-392
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC).Methods:This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models.Results:After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95% CI: 0.952-1.000) and 0.961 (95% CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95% CI: 0.893-0.959), 0.938 (95% CI: 0.876-1.000), respectively. Conclusions:The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR.
