Prevention, early detection and AI on international gynaecological cancer meeting agenda 

With so many exciting research developments in the pipeline, and an increased focus on national and international collaboration to help accelerate progress, the OCRF’s Research team is constantly working to keep abreast of what’s new.

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Why does this matter?

Ovarian cancer is the most lethal gynaecological cancer, so unsurprisingly it was a major focus of the meeting. Several themes were explored which are especially relevant to the OCRF’s work, from prevention and early detection through to artificial intelligence in research.

One of the most important sessions covered opportunistic salpingectomy - the removal of the fallopian tubes when people undergo surgery to reduce future ovarian cancer risk. Dr Gill Hanley traced the procedure’s origins in British Columbia in 2010, and the groundbreaking work done in Canada that has led to the highest level of uptake in the world. Dr Hanley took us through the robust and growing evidence base for its effectiveness, and why she feels it should be routinely offered to appropriate patients.

Early detection and Artificial Intelligence

Early detection was the focus of another important presentation, from Sudha Sundar, who explored how and whether ovarian cancer is being picked up during emergency admissions, and what happens next. Too often, delays are costing lives. She also presented the results of a large UK screening program that tested the long-standing biomarker CA125, and the results were clear: CA125 isn’t good enough. We need an early detection test that can identify ovarian cancer earlier, and more reliably, she told the conference. It was motivating to hear, as the OCRF continues to fund and advocate for research in this area.

A fascinating session on Artificial Intelligence (AI) was presented by Dr Ali Basherati, the Canadian lead on the global ‘AI Accelerator Grant’ co-funded by the OCRF. Ali presented exciting (but embargoed) Australian preliminary results from the study, explaining how AI ‘doesn’t see things we can’t see, it finds things we can’t find…and faster’.

Professor Iain McNeish’s presentation took this further, systematically exploring the opportunities and risks of AI and machine learning in research, and explaining why transparency and accountability are so important: ‘there should be no black boxes…we need to know how AI works’.