Integration of CA attention and KAN algorithm to predict EGFR mutation status in lung cancer

Epidermal Growth Factor Receptor (EGFR) mutations are critical biomarkers for targeted therapies in non-small cell lung cancer (NSCLC). However, conventional diagnostic methods rely on invasive tissue biopsies, which are costly, time-consuming, and pose significant limitations. As an alternative, non-invasive approaches using lung CT imaging to predict EGFR mutation status have gained attention for their rapid and user-friendly nature. This study explores EGFR mutation prediction by analyzing seven distinct 2D regions of interest (ROI): the nodule itself, 2-pixel, 4-pixel, and 6-pixel extensions around the nodule, CT slices containing the nodule, single-lung segmented images with the nodule, and bilateral lung segmented images with the nodule. We developed a deep learning model combining EfficientNet-B0 with a Coordinate Attention (CA) mechanism, replacing the traditional MLP classifier with a KAN classifier. Results show that analyzing single-lung images containing the nodule captures additional relevant information, achieving the highest predictive performance. This suggests that regions surrounding the nodule contain valuable discriminatory biomarkers. The model achieved 92.73% accuracy on the single-lung test set with only 6.82M parameters, demonstrating its potential as a clinical tool to optimize targeted treatment decision-making.