Predicting survival and molecular expression from histological images in lung cancer
We believe that computational learning systems have the potential of revealing new and yet unexploited information in tumor morphology. In our preliminary study, we showed that a deep learning-based system was able to accurately predict the ER status of breast cancer patients from H&E-stained histological images.
We plan to scale up this research in order to assess the potential of artificial intelligence to predict survival time and hormonal receptors expression (such as EGFR, ALK, and PDL-1) from H&E-stained images in lung cancer patients.
We envision the proposed technology to play a pivotal role in workflow analysis and targeted therapy. Similar to our preliminary study for receptor status prediction, survival time in lung cancer could potentially be accurately predicted. For those patients who would be classified with high morphological signals by the system, molecular identification using direct assays may be improved and even unnecessary.
1) Predict receptor status for different biomarkers in lung cancer patients, based on image analysis and convolutional neural networks.
2) Predict survival times in lung cancer patients, based on image analysis and convolutional neural networks.
Gil Shamai - Technion
Ron Kimmel - Technion