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• Clinical Article •
A MRI-Radiomics-based model predicts EGFR mutation status in brain metastases in lung cancer patients
LI Baoxun  PENG Yuqin  QIN Weifeng  XIAO Fang  LI Haojiang  CHEN Junwei  LI Jianing  HU Zhixuan  MAO Jiaji  SHEN Jun 

Cite this article as: LI B X, PENG Y Q, QIN W F, et al. A MRI-Radiomics-based model predicts EGFR mutation status in brain metastases in lung cancer patients[J]. Chin J Magn Reson Imaging, 2024, 15(3): 86-92. DOI:10.12015/issn.1674-8034.2024.03.015.


[Abstract] Objective To explore the value of a radiomics model based on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) magnetic resonance imaging (MRI) in non-invasive preoperative prediction of epidermal growth factor receptor (EGFR) mutation status in brain metastases of lung cancer patients.Materials and Methods We retrospectively collected clinical and MRI data of all lung cancer patients who underwent surgical resection of brain metastases and EGFR gene testing at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Sun Yat-sen University Cancer Center and Anhui Provincial Hospital from December 2016 to October 2021. A total of 103 patients (118 brain metastases) were included, with 80 patients (89 brain metastases) in the training set (Sun Yat-sen University Cancer Center and Anhui Provincial Hospital) and 23 patients (29 brain metastases) in the test set (Sun Yat-sen Memorial Hospital, Sun Yat-sen University). The enhanced edges of brain metastases were delineated on the CE-T1WI images to obtain the volume of interest (VOI), which was then copied to the registered T2WI images to obtain the T2WI-VOI of brain metastases. Radiomics features of brain metastases were extracted from these VOI using the PyRadiomics software, and feature selection was performed using mRMR and LASSO-logistics methods. A predictive model for EGFR mutation status was constructed, and the performance of the radiomics model in predicting EGFR mutations status was evaluated using the area under the receiver operating characteristic curve (AUC). The calibration curve and Hosmer-Lemeshow (HL) test were used to evaluate the calibration of the model, and decision curve analysis (DCA) was used to assess the clinical net benefit of the radiomics model in predicting EGFR mutation status.Results In the training set and test set, there were no statistically significant differences in age, sex, pathological type, number of brain metastases, and diameter of brain metastases between the EGFR mutation and wild-type groups in terms of clinical data and MRI features (P>0.05). A total of 3 190 radiomics features of brain metastases were extracted from the CE-T1WI and T2WI images. Finally, four radiomics features based on CE-T1WI and five radiomics features based on T2WI were selected, and a radiomics model for predicting EGFR mutation status was constructed using these features. The radiomics model showed good predictive performance with an AUC of 0.828, accuracy of 76.4%, sensitivity of 81.6%, and specificity of 70.0% in the training set and an AUC of 0.783, accuracy of 82.8%, sensitivity of 95.7%, and specificity of 33.3% in the test set. The calibration curves of the radiomics model in the training and test sets showed good consistency between the predicted probabilities and the actual probabilities of EGFR mutation status (HL>0.05). DCA demonstrated the clinical utility of the radiomics model within a threshold range of 13.8%-87.2%.Conclusions The radiomics model based on T2WI and CE-T1WI MRI can serve as an auxiliary tool for non-invasive preoperative prediction of EGFR mutation status in brain metastases of lung cancer patients.
[Keywords] lung cancer；brain metastases；epidermal growth factor receptor；radiomics；magnetic resonance imaging

LI Baoxun1   PENG Yuqin2   QIN Weifeng1   XIAO Fang3   LI Haojiang4   CHEN Junwei1   LI Jianing1   HU Zhixuan1   MAO Jiaji1   SHEN Jun1*  

1 Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China

2 Department of Radiology, Shenshan Medical Center, Memorial Hospital of Sun Yat-Sen University, Shanwei 516600, China

3 Department of Radiology, the First Affiliated Hospital of USTC, Anhui Provincial Hospital, Hefei 230036, China

4 Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China

Corresponding author: SHEN J, E-mail: shenjun@mail.sysu.edu.cn

Conflicts of interest   None.

Received  2023-11-18
Accepted  2024-02-27
DOI: 10.12015/issn.1674-8034.2024.03.015
Cite this article as: LI B X, PENG Y Q, QIN W F, et al. A MRI-Radiomics-based model predicts EGFR mutation status in brain metastases in lung cancer patients[J]. Chin J Magn Reson Imaging, 2024, 15(3): 86-92. DOI:10.12015/issn.1674-8034.2024.03.015.

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