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• Clinical Article •
Value of T1WI enhanced radiomics model for predicting EGFR mutations in non-small cell lung cancer
HUANG Jinxiang  CHEN Jieyun 

Cite this article as: HUANG J X, CHEN J Y. Value of T1WI enhanced radiomics model for predicting EGFR mutations in non-small cell lung cancer[J]. Chin J Magn Reson Imaging, 2023, 14(11): 42-47. DOI:10.12015/issn.1674-8034.2023.11.008.


[Abstract] Objective To explore the predictive value of T1WI-enhanced radiomics model for non-small cell lung cancer brain metastases in predicting epithelial growth factor receptor (EGFR) mutation status in non-small cell lung cancer.Materials and Methods The imaging data of cranial magnetic resonance non-contrast scan + contrast examination of 97 patients with non-small cell lung cancer brain metastases before treatment were retrospectively analyzed (50 EGFR mutant and 47 EGFR wild type), and randomly grouped into training group and test group according to 8 : 2. The radiomics features were extracted from the T1WI-enhanced transverse, coronal and sagittal positions, and the dimensionality reduction and screening of the radiomics features were successively carried out by VarianceThreshold, SelectKBest and least absolute shrinkage and selection operator (LASSO), and the support vector machines (SVM) and logistic regression (LR) were used for classifier modeling, and cross-validation by 5-fold method, finally the performance of the prediction model was evaluated in the test group, the receiver operating characteristic (ROC) curve of the training group and the test group was drawn to evaluate the prediction efficiency, and the difference between the models is compared by DeLong test.Results The AUCs of T1WI enhanced transverse, coronal and sagittal radiomics models reached 0.64, 0.68 and 0.80, respectively. The AUC of the combined sequence model test group can reach 0.84, among which the LR classifier has the best prediction efficiency, the AUC, sensitivity, specificity and accuracy of the training group are 0.86, 74%, 75% and 76%, respectively, and the AUC, sensitivity, specificity and accuracy of the test group are 0.84, 80%, 78% and 80%, respectively, and the DeLong test between the models has no significant significance (P＞0.05).Conclusions Radiomics model based on T1WI enhanced transverse, coronal and sagittal positions can predict EGFR mutation status, and the LR classifier model combined with sequence has the best prediction efficiency, which is helpful to guide the rational selection of targeted drug therapy and individualized precision medicine in clinical practice.
[Keywords] brain metastases；non-small cell lung cancer；radiomics, magnetic resonance imaging；epidermal growth factor receptor

HUANG Jinxiang1   CHEN Jieyun2*  

1 Department of Radiology, Affiliated Zhangzhou Hospital of Fujian Medical University, Zhangzhou 363005, China

2 Department of Radiology, Affiliated Quanzhou Frist Hospital of Fujian Medical University, Quanzhou 362000, China

Corresponding author: CHEN J Y, E-mail: 2207934327@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Fujian Province (No. 2022J011463).
Received  2023-07-02
Accepted  2023-11-06
DOI: 10.12015/issn.1674-8034.2023.11.008
Cite this article as: HUANG J X, CHEN J Y. Value of T1WI enhanced radiomics model for predicting EGFR mutations in non-small cell lung cancer[J]. Chin J Magn Reson Imaging, 2023, 14(11): 42-47. DOI:10.12015/issn.1674-8034.2023.11.008.

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