Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a...

Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCLC) accounts for 85% of cases, with oncogenic alterations like EGFR, ALK, ROS1, and KRAS guiding targeted therapies. Their prevalence varies by ethnicity, smoking status, and gender. Advances in artificial intelligence (AI) enable molecular biomarker prediction from hematoxylin and eosin-stained whole-slide images (H&E WSIs), offering a non-invasive approach to precision oncology. This review assesses deep learning (DL) models predicting oncogenic drivers in NSCLC from H&E WSIs and their diagnostic accuracy.

A systematic review registered in PROSPERO (CRD42024573602) was conducted in Embase, LILACS, Medline, Web of Science, and Cochrane to identify studies on DL models using H&E slides for LC gene alterations. Only English and Spanish studies were included. Key metrics were extracted for meta-analysis. Studies without LC-specific data, missing essential metrics, or with inconsistent results were excluded.

We found evidence that convolutional neural networks (CNNs) were the most common architectures in studies. Also, in the meta-analysis, ALK {sensitivity of 84% [95% confidence interval (CI): 62-95%] and specificity of 85% (95% CI: 55-96%)}, EGFR [80% (95% CI: 72-86%) and specificity of 77% (95% CI: 69-83%)] and TP53 [sensitivity and specificity of 70% (95% CI: 65-83%)] were the oncogenic driver molecular alterations that demonstrated the best predictive capability performance.

Our results emphasize the potential of these models as screening tools despite H&E WSI.It is necessary to validate these predictive models among diverse populations and clinical outcomes. This approach is crucial and leaves an open door for advances in precision medicine, offering promising avenues for personalized treatment strategies.