Predicting EGFR mutation subtypes in lung adenocarcinoma using 18F-FDG PET/CT radiomic features

Abstract

Background: Identification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (¹⁸F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.

Methods: We retrospectively studied ¹⁸F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).

Results: Two sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.

Conclusions: In our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

Keywords: 18F-FDG PET/CT; EGFR mutation subtypes; Lung adenocarcinoma; prediction; radiomic features.

Figure 1

The workflow of our study. (A) Tumor region of interest (ROI) was segmented by experienced radiologists; (B) radiomic features were extracted from original images and image components after wavelet transformation; (C) prediction of the EGFR mutation.

Figure 2

Selected features in predicting E19 del mutation. (A) Feature importance of selected features; (B) correlation heatmap of selected features. f0, CT-wl-fo-Ske; f2, CT-wl-glszm-SZNUN; f3, CT-wl-glszm-SALGLE; f4, PET-orig-gldm-DNU; f1, CT-wl-gldm-LGLE.

Figure 3

Selected features in predicting E21 mis mutation. (A) Feature importance of selected features; (B) correlation heatmap of selected features. f7, CT-wl-gldm-LDHGLE; f6, CT-wl-fo-Mean; f5, CT-orig-fo-Max; f8, CT-wl-fo-Median; f9, PET-orig-glcm-CS.

Figure 4

Receiver operating characteristic curve for the predictive model of E19 del mutation and E21 mis mutation in the test cohort.

Figure 5

Receiver operating characteristic curve for the EGFR model in the train cohort and test cohort.

Figure 6

(A) Receiver operating characteristic curve for the predictive model of E19 del mutation; (B) receiver operating characteristic curve for the predictive model of E21 mis mutation.

Figure 7

Receiver operating characteristic curve for the predictive model of E19 del mutation on two hospital subgroups.

Figure S1

The heatmap of the selected features.