TIA paper on prediction of colon cancer mutations and DNA mismatch repair deficiency
· To treat colorectal cancers efficiently it’s essential that the molecular pathways involved in the development and key mutations of the cancer must be determined
· Currently genetic tests are done to detect mutations and pathways, which can be costly and may take weeks
· A new machine learning algorithm with high accuracy and requiring no manual annotations has been proposed by researchers at the University of Warwick, which can detect three key molecular pathways in colorectal cancer
· The findings open up the possibility to select patients likely to benefit from targeted therapies at lower costs and with quicker turnaround times as compared to current methods
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.
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.
In the paper, ‘A weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images’, published on the 19th of October, in the journal The Lancet Digital Health, researchers from the University of Warwick have explored how machine learning can detect three key mutations from whole-slide images of Colorectal cancer slides stained with Hematoxylin and Eosin, as an alternate to current testing regimes for these pathways and mutations.
The researchers propose a novel iterative draw-and-rank sampling algorithm, which can select representative sub-images or tiles from a whole-slide image without needing any detailed annotations at cell or regional levels by a pathologist. 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.
For more info, see the university press release: https://warwick.ac.uk/newsandevents/pressreleases/new_deep_learning