Scientists say they’ve developed an early screening test that may flag ovarian cancer with extreme accuracy — thanks to breakthroughs in artificial intelligence.
The 93% accurate machine learning approach “represents a promising recent direction within the early detection of ovarian cancer, and maybe other cancers as well,” said John McDonald, a Georgia Tech professor emeritus.
Early detection is particularly necessary, as Georgia Tech researchers describe ovarian cancer as a “silent killer” that’s typically asymptomatic at first — rarely can it’s found during a routine pelvic exam.
Georgia Tech scientists say a girl’s metabolic profile might be used to determine an accurate likelihood of getting ovarian cancer.
“This personalized … approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests,” McDonald said.
![Machine learning is proving to be a driving force in the detection of ovarian cancer.](https://nypost.com/wp-content/uploads/sites/2/2024/01/iStock-1607698226.jpg)
Ovarian cancer is a number one reason for cancer deaths in women.
The American Cancer Society reports that about 1 in 87 women can be stricken with ovarian cancer, and 1 in 130 will likely die from it.
Warning signs include bloating, belly pain, difficulty eating, and frequent urination.
A rectovaginal pelvic exam to discover abnormalities, a transvaginal ultrasound for pelvic pictures, and/or a CA-125 blood test to measure the presence of a certain protein could also be needed to determine if a girl has ovarian cancer.
When treated early, the survival rate for five years is over 90%, Georgia Tech noted.
The university’s study was published within the March online issue of the journal Gynecologic Oncology.
![The process might be applicable to screen for other cancers as well.](https://nypost.com/wp-content/uploads/sites/2/2024/01/GettyImages-1258734594.jpg?w=1024)
Researchers focused their efforts on metabolites — molecules produced from chemical processes — in blood.
Typically, the doubtless game-changing metabolites have been identified in broad groupings quite than as individual entities, co-author Jeffrey Skolnick explained.
Lower than 7% of them in blood have been chemically characterised, but machine learning — paired with the analytical strategy of mass spectrometry — have allowed researchers to discover unique characteristics that may pave the way in which for an ovarian cancer diagnosis, added co-author Dongjo Ban.
He said that with the brand new approach, hundreds of metabolites “might be readily and accurately detected,” so there might be “an accurate ovarian cancer diagnostic.”
“Clearly, there’s an amazing need for an accurate early diagnostic test for this insidious disease,” added McDonald.
The research team is optimistic that this recent methodology, tested on 564 women, can lead to early screening for other forms of cancer as well.