Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features

Abstract

Purpose: The aim of this study was to develop a novel approach for predicting the expression status of Epidermal Growth Factor Receptor (EGFR) and its subtypes in patients with Non-Small Cell Lung Cancer (NSCLC) using a Three-Dimensional Convolutional Neural Network (3D-CNN) ConvNeXt, radiomics features and clinical features.

Materials and methods: A total of 732 NSCLC patients with available CT imaging and EGFR expression data were included in this retrospective study. The region of interest (ROI) was manually segmented, and clinicopathological features were collected. Radiomic and deep learning features were extracted. The instances were randomly divided into training, validation, and test sets. Feature selection was performed, and XGBoost was used to create solo models and combined models to predict the presence of EGFR and subtypes mutations. The effectiveness of the models was assessed using ROC and PRC curves.

Results: We established the following models: Model_CNN, Model_radiomic, Model_clinical, Model_CNN+radiomic, Model_CNN+clinical, Model_{radiomic+clinical}, and Model_{CNN+radiomic+clinical}, which were based on deep learning features, radiomic features, clinical data and combinations of these, respectively. In predicting EGFR mutations, Model_{CNN+radiomic+clinical} demonstrated superior performance compared to other prediction models, achieving an AUC of 0.801. For distinguishing between EGFR subtypes ex19del and L858R, Model_CNN+radiomic reached the highest AUC value of 0.775.

Conclusions: Both deep learning models and radiomic signature-based models offer reasonably accurate non-invasive predictions of EGFR status and its subtypes. Fusion models hold the potential to enhance noninvasive methods for predicting EGFR mutations and subtypes, presenting a more reliable prediction approach.

Keywords: CT; EGFR; NSCLC; deep learning; radiomic.