Artificial Intelligence to Map Ovarian Cancer Risks in Women

Artificial Intelligence to Map Ovarian Cancer Risks in Women

The University of South Australia will conduct a global study that will use artificial intelligence to map ovarian cancer risks in women to identify them and treat the patients sooner.

Due to nonspecific symptoms and few recognized causes, ovarian cancer is frequently discovered late, with a five-year survival rate of nearly 30% for women with late-stage cancer.

Professor Elina Hypponen, a renowned nutritional epidemiologist, and a team from the Australian Centre for Precision Health (UniSA) have been awarded $1.2 million by the Federal Government to map the physical and genetic risks of ovarian cancer.

The study’s ambition is that a machine learning model will effectively anticipate which women will get ovarian cancer within the next 15 years by automatically analyzing data and identifying risk patterns. Diet, genes, and behavior all have a role, and it’s believed that a computational method will help identify those who are most at risk.

According to professor Hypponen, the team can significantly enhance ovarian cancer survival rates if they diagnose it early. Therefore, if women most at risk are identified sooner, they can be prioritized for more intensive screening, resulting in an earlier diagnosis and better prognosis.

Professor Hypponen also claims that age, endometriosis, ovulation, and obesity are all risk factors. Interestingly, there’s been a lot of interest in investigating whether these risks may be lowered with hormones or other drugs like oral contraceptives or aspirin. She adds that evidence suggests that women might be able to alter their ovarian cancer risk simply by changing their diet.

This project is the first and most extensive study of ovarian cancer globally to incorporate such a thorough examination of risk variables. The team is hopeful that progress in the prevention and early detection of ovarian cancer will happen soon.

Photo by National Cancer Institute on Unsplash

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