Issued by: Staff writer – Joan Hendricks
Next month on Wednesday, 8 May is World Ovarian Cancer Day aiming to create a community where those living with ovarian cancer along with their families as well as survivors of the disease can help educate their communities about the illness.
In light of this, a recent study done by researchers from Stanford University’s School of Medicine shows that not enough American women with ovarian cancer are getting genetic testing for cancer-related mutations. Genetic testing, also known as DNA testing, allows the determination of bloodlines and the genetic diagnosis of vulnerabilities to inherited diseases.
In an article on Survivornet, Dr Ursula Matulonis, Chief of the Division of Gynecologic Oncology at Dana-Farber Cancer Institute says: “All women with ovarian cancer at time of diagnosis, not at recurrence, but at the time of diagnosis, should undergo genetic testing regardless of family history, the patient’s age and histology.”
And with recent statistics predicting that deaths will likely increase by 67% by 2035 due to this particular cancer, it might be in the patient’s best interest as testing for the presence of mutations could be used to guide health care decisions that could save more lives.
AI technology can predict death
Trying to reduce this figure is crucial so it’s a relief to hear that recently in the UK, new AI technologies have been uncovered that can help to predict what treatment might be more effective for patients in future.
According to an article on IOL, a mathematical software tool, TEXLab can be used to identify patients who are unlikely to respond to standard treatments and offer alternatives.
“Long-term survival rate for patients with advanced ovarian cancer is poor despite advancements in treatments. There is an urgent need for new ways,” said lead author Eric Aboagye, Professor at Imperial College London.
The findings, published in Nature Communications, showed that the software was up to four times more accurate for predicting deaths from ovarian cancer than standard methods.
“Our technology is able to give clinicians more detailed and accurate information on how the patients are likely to respond to different treatments, which could enable them to make better and more targeted treatment decisions,” said Aboagye. The researchers will carry out a larger study to see how accurately the software can predict the outcomes of surgery and/or drug therapies for individual patients.