Lymphedema is an incurable condition caused by the loss of function in a lymph node. When this happens, lymph fluid is no longer as easily moved and released from the body and stays trapped in one area. In breast cancer patients who undergo mastectomies with lymph node dissection, lymphedema is a common result and generally impacts one or both arms.
More than 41 percent of breast cancer patients who’ve undergone surgery will be faced with lymphedema symptoms within 10 years.
Lymphedema serves as a constant reminder of the cancer battle these patients struggled with. It requires continual care to ensure that it doesn’t progress to the later and more dangerous stages.
The sooner lymphedema is diagnosed and treated, the better, because it can be devastating to the patient’s health in cases where it isn’t properly managed. If not treated, lymphedema can result in pain, loss of mobility, and even death.
Until recently, the only way to diagnose lymphedema was to wait until visible swelling showed up. Then a doctor could see and feel that the lymph was not properly flowing out of the body and prescribe a plan for treating the disease. But lymphedema can be causing problems long before the swelling rears its ugly head.
“Clinicians often detect or diagnose lymphedema based on their observation of swelling. However, by the time swelling can be observed or measured, lymphedema has typically occurred for some time, which may lead to poor clinical outcomes,” says lead author Mei R Fu, PhD, RN, FAAN, and associate professor of nursing at NYU Meyers.
So how do we diagnose a disease that isn’t detectable to the senses? We invent a machine to do the job for us.
One of the latest artificial intelligence systems, known as “artificial neural network,” is tasked with constructing algorithms that can predict lymphedema cases. It can do this job with 93 percent accuracy, which is much better than doctors are able to on their own. Plus, it is capable of making its diagnosis much earlier than a doctor could, because it doesn’t rely on palpable symptoms that a doctor can see and feel.
So what does the machine use to make its predictions then? Well, the removal of lymph nodes is a good start, followed by patient-reported symptoms like slight heaviness and tightness of the skin (and 24 other potential symptoms). These early signs are monitored over a period of time, leaving the machine with a better understanding of whether or not the patient is progressing toward healing from their mastectomy without lymphedema or whether the symptoms of lymphedema continue to grow, however slightly.
Because the machine can monitor patient symptoms in real time from home, unnecessary trips to the doctor could be avoided, and patients are more likely to track their symptoms. They can also be warned early if they’re at risk for lymphedema so that they can schedule a visit with a physician to follow up on the issue and talk about care and maintenance of the disease.
Researchers believe this tool will improve lymphedema outcomes in the future, allowing breast cancer patients and others who are at risk for the condition to more easily monitor it and live more normal lives. It’s just one more way to keep cancer from controlling lives, and we’re always on board with that.
Elizabeth Nelson is a wordsmith, an alumna of Aquinas College in Grand Rapids, a four-leaf-clover finder, and a grammar connoisseur. She has lived in west Michigan since age four but loves to travel to new (and old) places. In her free time, she. . . wait, what’s free time?