It’s called the silent killer. There’s no simple test for it, and often no early symptoms, or symptoms that appear to be something else. And so ovarian cancer, operating in secret, kills 15,000 American women each year. New diagnostic techniques, however, are helping to bring this hidden illness into the light.
Recent research, in addition, is starting to help provide explanations for how the cancer works. The cancer cells are able to influence genes in the surrounding tissue to help create an environment that is comfortable for tumors. This may be part of the reason ovarian cancer is particularly prone to appear to go into remission for a while, only to come back, strong as ever, to wreak further havoc.
Exacerbating this is that the immune system, normally responsible for fighting cancers, may actually be helping ovarian cancer along. Cells called dendritic cells, which in a healthy immune system are an important part of the process by which the T cells, which attack invaders learn to recognize those invaders, appear to actively hide the tumor cells—while still functioning normally in all other ways. Scientists are not yet sure how or why these cells betray the body and assist the cancer.
Once ovarian cancer strikes, the symptoms are similar to quite a few other conditions. Symptoms include abdominal or pelvic pain, changes in bowel or bladder habits, bloating, loss of appetite, fatigue, and low back pain. While these symptoms are cause to see the doctor, particularly if you have a family history of ovarian or breast cancer or know you have a particular genetic vulnerability, the only real test is to examine the ovaries directly for tumors.
That may soon change, however. A new imaging technique combines three different tools to get an unprecedented look inside the body, which is giving diagnostic personnel the best chance yet of spotting precancerous conditions. The strengths of the various techniques—contrast of one, high resolution of another, deep-tissue capabilities of the third—combine into something greater than the sum of its parts.