Predict EGFR Mutation Status on CT Images Using Texture and Contour Enhanced Masked Autoencoders

The EGFR mutation status significantly influences targeted therapy for non-small cell lung cancer. In recent years, there has been significant progress in non-invasive EGFR mutation status prediction studies based on chest CT images. However, these studies commonly rely on extensive private data for training rather than small-scale publicly available dataset, thereby failing to overcome the dependency on high-cost large-scale annotated data. Additionally, these studies generally neglect the texture and contour features with strong discriminative power, leading to insufficient performance. This paper proposes a two-stage framework for EGFR mutation status prediction. Initially, we utilize self-supervised Masked Autoencoders (MAE) to pre-train the encoder on in-domain chest CT images, overcoming the problem of insufficient annotated data to reduce the dependency on high-cost annotated data. Subsequently, fine-tune the encoder to make it suitable for downstream EGFR prediction. Simultaneously, we propose texture and contour enhanced MAE (TCMAE), designing Multi-layer Features Aggregation Module (MFAM) to fully exploit multi-layer semantic features, introducing Texture and Contour Prediction Module (TCPM) to enhance the model’s capability in extracting texture and contour features through multitask learning, utilizing Modified Spectral Block (MSB) to adjust the weighting between high and low frequency features. Experiments demonstrate that despite using only a small-scale public dataset, NSCLC-Radiogenomics, the proposed method still achieves high accuracy.