Sumary of Deep learning algorithm can accurately detect molecular pathways, key mutations in colorectal cancer:
- A new deep learning algorithm created by researchers from the University of Warwick can pick up the molecular pathways and development of key mutations causing colorectal cancer more accurately than existing methods, meaning patients could benefit from targeted therapies with quicker turnaround times and at a lower cost.
- In order to quickly and efficiently treat colorectal cancer the status of molecular pathways involved in the development and key mutations of the cancer must be determined.
- However, researchers from the Department of Computer Science at the University of Warwick have been exploring how machine learning can be used to predict the status of three main colorectal cancer molecular pathways and hyper-mutated tumours.
- A key feature of the method is that it does not require any manual annotations on digitized images of the cancerous tissue slides.
- Essentially the new algorithm can leverage the power of raw pixel data for predicting clinically important mutations and pathways for colon cancer, without human interception.
- Iterative draw-and-rank sampling works by training a deep convolutional neural network to identify image regions most predictive of key molecular parameters in colorectal cancers.
- A key feature of iterative draw-and-rank sampling is that it enables a systematic and data-driven analysis of the cellular composition of image tiles strongly predictive of colorectal molecular pathways.