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

我们的项目将确定在肿瘤中发现的与癌症相关的基因突变是否可以通过医学手段检测出来

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.