Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma

Abstract

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.

Development and validation of novel radiomics-based nomograms for the prediction of EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer

“…By analyzing ROC curves, calibration curves and decision curves, we found that our novel radiomic models not only had high accuracy and robustness, but they also had high clinical gain. As shown in Table S5, several former studies (32,(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) also tried to build radiomics-based classifiers to predict EGFR mutation or Ki-67 PI expression in lung cancer.…”

Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer

“…The results of this study confirmed the reliability of radiomic and deep learning models for the non-invasive prediction of EGFR mutation status in lung adenocarcinoma with a high degree of accuracy. In lung adenocarcinoma patients, two previous studies (39,40)combined both radiomic and clinical features to successfully build a radiomic-clinical model that could efficiently identify EGFR mutant phenotypes from wild types with good AUCs of 0.779 and 0.823. However, two other studies (41,42) also successfully built a combined radiomic-clinical prediction model but also found that a deep learning feature-based model could also predict EGFR gene mutation status in patients with lung adenocarcinoma in a more accurate manner, achieving AUCs of 0.810 and 0.758.…”

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images

“…In the era of precision medicine, there is a trend that patients with lung cancer are treated only after having their gene expression clarified. Some previous studies have used deep learning technology to predict EGFR, PD-L1, or ALK gene status, respectively, and achieved favorable performances (Table 3) [18,19,[29][30][31][32][33][34]. However, these previous studies focused specifically on predicting mutation status in only one gene.…”