Discover how the cutting-edge Vision Transformer technology is revolutionizing the prediction of EGFR mutation status, offering new hope for personalized treatment strategies in lung adenocarcinoma patients.
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Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.
Weng et al., Clin Transl Oncol 2024
DOI: 10.1007/s12094-023-03366-4
The study introduces a deep learning model based on a self-attention-based ViT-B/16 architecture to predict EGFR mutation status in lung adenocarcinoma patients using non-invasive CT images. This approach aims to overcome the limitations of conventional genotyping methods.
What’s new: The model was trained and validated on an internal dataset of 525 patients and tested externally on 30 patients from a public dataset. It uses Grad-CAM to generate attention maps, highlighting areas of interest related to EGFR mutations.
Importance: Accurate prediction of EGFR mutation status is critical for tailoring appropriate treatments for lung adenocarcinoma patients.
Contributions to literature: The model showed high performance with an accuracy of 0.848, AUC of 0.868, sensitivity of 0.924, and specificity of 0.718 in the internal validation cohort. In the external test cohort, it achieved an accuracy of 0.833, AUC of 0.885, sensitivity of 0.900, and specificity of 0.800. These results suggest that the model could be a valuable tool for clinicians in the non-invasive assessment of EGFR mutation status.