A Quarter-split Domain-adaptive Network for EGFR Gene Mutation Prediction in Lung Cancer by Standardizing Heterogeneous CT image

Epidermal growth factor receptor (EGFR) gene mutation status is crucial for the treatment planning of lung cancer. The gold standard for detecting EGFR mutation status relies on invasive tumor biopsy and expensive gene sequencing. Recently, computed tomography (CT) images and deep learning have shown promising results in non-invasively predicting EGFR mutation in lung cancer. However, CT scanning parameters such as slice thickness vary largely between different scanners and centers, making the deep learning models very sensitive to noise and therefore not robust in clinical practice. In this study, we propose a novel QuarterNetadaptive model to predict EGFR mutation in lung cancer, which is robust to CT images of different thicknesses. We propose two components: 1) a quarter-split network to sequentially learn local lung features from different lung lobes and global lung features; 2) a domain adaptive strategy to learn CT thickness-invariant features. Furthermore, we collected a large dataset including 1413 patients with both EGFR gene sequencing and CT images of various thicknesses to evaluate the performance of the proposed model. Finally, the QuarterNetadaptive model achieved AUC over 0.88 regarding CT images of different thicknesses, which improves largely than state-of-the-art methods.Clinical relevance—We proposed a non-invasive model to detect EGFR gene mutation in lung cancer, which is robust to CT images of different thicknesses and can assist lung cancer treatment planning.