Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis

Background

Epidermal growth factor receptor (EGFR) mutations play a pivotal role in guiding targeted therapy for lung cancer, making their accurate detection essential for personalized treatment. Recently, artificial intelligence (AI) has emerged as a promising tool for identifying EGFR mutation status from digital pathology images. This systematic review and meta-analysis evaluate the diagnostic accuracy of AI models in predicting EGFR mutations from whole slide images (WSIs) in lung cancer patients.

Methods

A comprehensive search was conducted across four databases (EMBASE, PubMed, Web of Science, and Scopus) for studies published up to June 20th, 2024. Studies employing AI algorithms, including machine learning and deep learning techniques, to predict EGFR mutations from digital pathology images were included. The risk of bias and applicability concerns were assessed using the QUADAS-AI tool. Diagnostic accuracy metrics such as sensitivity, specificity, and the Area Under the Curve (AUC) were extracted. Random-effects models were applied to synthesize the AI model performance. This study is registered with PROSPERO (CRD42024570496).

Results

Out of 1,828 identified studies, 16 met the inclusion criteria, with 4 eligible for meta-analysis. The pooled results demonstrated that AI algorithms achieved an AUC of 0.756 (95% CI: 0.669–0.824), a sensitivity of 66.3% (95% CI: X–Y), and a specificity of 68.1% (95% CI: X–Y). Subgroup analyses revealed that AI models using in-house datasets, surgical resection samples, the ResNet architecture, and tumor-focused regions exhibited improved predictive performance.

Conclusion

AI models exhibit potential for non-invasive prediction of EGFR mutations in lung cancer patients using WSIs, although current accuracy and precision warrant further refinement. Future research should aim to enhance AI algorithms, validate findings on larger datasets, and integrate these tools into clinical workflows to optimize lung cancer management.