Can deep-learning algorithms identify genetic mutations or aberrant cellular signalling pathways from medical images?

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Our project will determine if cancer-related genetic mutations found in tumours can be detected using medical**imaging. Tumours arise from a series of genetic errors, and these determine much of the behaviour of a tumour,**including its aggressiveness and its response to a treatment. In day-to-day medical imaging, once a patient has**undergone a medical scan, a specialist will look at the images and provide a diagnosis (e.g., liver cancer).**Sometimes, a biopsy (tumour sample) is acquired to better determine the subtype of cancer. Our project aims at**assisting physicians by providing them with additional information extracted using artificial intelligence and**advanced computer software. These are already known to be superior to humans in finding and quantifying**subtle image characteristics. We hypothesize that these image characteristics could be predictive of the cancer**subtype and its optimal treatment. First, this software has to be trained to recognize mutations. Because human**tumours vary a lot, it is difficult to differentiate visual characteristics caused by an individual inherent**variability from those caused by the mutation. To overcome this, we will use genetically engineered mouse**models - these will have specific mutations that will result in cancer but with limited variability between**animals. This will allow us to train a software to recognize tumours that have specific mutations. If successful,**our project will ultimately lead to software tools with capabilities similar to biopsies, and better and less**invasive management of cancer.