The ProCan® cancer research program at CMRI is analysing thousands of different types of proteins (the proteome) in childhood and adult cancers to help cancer clinicians match their patients with the best treatment available. They are a step closer to that goal with this study that involves 30 collaborating research groups in six countries (Austria, Australia, Canada, Greece, Spain and the USA), and cancer proteomic data obtained by the ProCan team from 7,525 cancers, which is the largest set of cancer proteomes generated in a single centre.

The reason that the size of the dataset matters is that predicting how a cancer will behave based on the proteome, including how the cancer will respond to treatment, requires advanced computational techniques. This includes AI, which needs to be trained on large datasets that include both the proteome and clinical information about the patient. However, data privacy regulations and other restrictions on the transfer of data across geographical boundaries make it challenging to assemble large sets of patient data, especially when multiple countries are involved.

Using an AI technique called federated deep learning, they trained AI models on datasets stored at several local sites held behind firewalls. Instead of sharing clinical data, these AI models were sent to a central server to update a global model. Repeating this process multiple times resulted in a diagnostic test that has essentially the same accuracy as when the data was all brought together in one centralised database.

Source: https://www.cmrijeansforgenes.org.au/news/ai-used-to-analyse-cancer-samples-to-improve-treatment-outcomes