19 Annual Report 2021 2021/22 MRFF Collaborative grants Associate Professor Carlos Salomon, The University of Queensland Implementing a rapid blood test for the early detection of ovarian cancer. Associate Professor Carlos Salomon and his team previously undertook a discovery project, funded by the OCRF, which identified a new type of test that showed promising results for identifying ovarian cancer in its early stages, including asymptomatic stages. This test is focused on identifying High Grade Serous Ovarian Cancer which is responsible for most ovarian cancer mortality. The biomarkers identified were also associated with the extracellular signalling pathway, used by cancer cells to promote metastatic activity, which is important for understanding the way ovarian cancer spreads. This project will seek to trial the efficacy of this early detection approach on greater sample numbers. In recognition of the OCRF’s continued support for the project, Associate Professor Salomon has named the algorithm test the OCRF-7. Professor Elina Hypponen, The University of South Australia Predicting and Preventing Ovarian Cancer: a machine learning approach. Professor Elina Hypponen aims to understand what causes ovarian cancer and how we can identify women at high risk. Her project will use a data-driven machine learning approach to map genetic and physical risk factors that increase the likelihood of developing ovarian cancer. Machine-learning is a form of artificial intelligence that allows many factors to be considered simultaneously so that patterns of risk, that could lead to predicting ovarian cancer, can be uncovered. we have committed over $1 million dollars for this new research into early detection and treatment.