According to a story from The Daily Texan, professor Marian Williams-Brown from the University of Texas is conducting research in an effort to develop a more effective approach for detecting ovarian cancer. Part of the motivation for this research is the story of another employee at the university named Rebecca. She was able to have her ovarian cancer diagnosed early on after complaints of abdominal pain. Rebecca was treated successfully, but many patients are not so fortunate.
About Ovarian Cancer
Ovarian cancer can appear on or within the ovary. Ovarian cancer rarely causes distinctive symptoms in its early stages, so many patients are often diagnosed with advanced disease. The risk of getting ovarian cancer is connected to how long a woman has ovulated during her life; women who ovulate for longer periods are at greater risk. Late menopause or early puberty are risk factors, as are not having children, fertility medication, certain genetic variants and mutations (such as BRCA mutations), and exposure to talc, herbicides, and pesticides. Some symptoms of ovarian cancer include fatigue, bloating, a feeling of fullness, loss of appetite, indigestion, abdominal swelling, and pelvic pain. Treatment can include chemo, radiation, surgery, hormone therapy, and immunotherapy. There are many different kinds of ovarian cancer. Five year survival rate is 45 percent in the US. To learn more about ovarian cancer, click here.
Improving Detection
Marian’s approach for improving ovarian cancer detection involves the creation of an algorithm that takes into account a person’s age and their levels of CA125, a protein which, when it appears at abnormally high levels, is often an indicator of ovarian cancer. This algorithm could potentially detect ovarian cancer with substantially greater accuracy in comparison to current techniques, which include ultrasounds and blood tests.
CA125 levels tend to fluctuate, particularly when someone is experiencing hormonal changes. In order to develop the algorithm, Marian plans to monitor CA125 levels in a group of post-menopausal women. The algorithm will assign a person a score, which will then be compared to other measures to determine if they have cancer. Marian also hopes that she will be able to identify additional possible indicators ovarian cancer during the study, which could also aid in earlier diagnosis.
