Machine learning helps detect lymphedema among breast cancer survivors

June 7, 2018, New York University

Machine learning using real-time symptom reports can accurately detect lymphedema, a distressing side effect of breast cancer treatment that is more easily treated when identified early, finds a new study led by NYU Rory Meyers College of Nursing and published in the journal mHealth.

"Using a well-trained classification algorithm to detect based on real-time symptom reports is a highly promising tool that may improve lymphedema outcomes," said Mei R Fu, Ph.D., RN, FAAN, associate professor of nursing at NYU Meyers and the study's lead author.

Lymphedema is a build-up of lymph fluid that causes swelling in the arms or legs and is commonly caused by the removal of lymph nodes as part of cancer treatment. It can occur immediately after cancer surgery or as late as 20 years after surgery; a recent study found that more than 41 percent of patients experienced lymphedema in their arms within 10 years of their surgery.

Lymphedema is one of the most dreaded adverse effects from because of its chronic nature and debilitating symptoms, including arm swelling, heaviness, tightness, achiness, stiffness, burning, and decreased mobility. While there is no cure for lymphedema, early detection and intervention can reduce symptoms and keep it from worsening, although early detection remains a challenge.

"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," said Fu.

"In our digital era, integrating technology into health care has led to advances in detecting and predicting various medical conditions," said Yao Wang, Ph.D., professor of electrical and computer engineering at NYU Tandon School of Engineering and the study's coauthor.

A type of artificial intelligence, machine learning is of interest to researchers due to its ability to construct algorithms that continually improve predictions and generate automated knowledge through data-driven predictions or decisions with incoming data—in this case, symptom reports. Machine learning is particularly beneficial when there are many relevant factors that are not independent, which is true for lymphedema symptoms.

In this study, the researchers used a web-based tool to collect information from 355 women who had undergone treatment for breast cancer, including surgery. In addition to sharing demographic and clinical information, including whether they had been diagnosed with lymphedema, participants were asked whether they were currently experiencing 26 different lymphedema symptoms.

Statistical and machine learning procedures were performed for data analysis. Five different classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model, , and support vector machine. The algorithms were also compared with a conventional statistical approach that determines the optimal threshold for the count based on the receiver operating curve.

The researchers found that all five approaches outperformed the standard statistical approach, and the artificial neural network achieved the best performance for detecting lymphedema. The artificial neural network was 93.75 percent accurate, correctly classifying patients to have true lymphedema cases or non-lymphedema cases based on the symptoms reported.

"Such detection accuracy is significantly higher than that achievable by current and often used clinical methods," said Fu.

The researchers note that conducting such real-time lymphedema assessment encourages patients to monitor their lymphedema status without having to visit a healthcare professional. Based on patients' symptoms and resulting risk for lymphedema, the assessment system could alert patients at risk to schedule in-person clinical visits for further evaluation. This may lessen the burden of unnecessary clinical visits on patients and the healthcare system.

"This has the potential to reduce healthcare costs and optimize the use of healthcare resources through early lymphedema detection and intervention, which could reduce the risk of lymphedema progressing to more severe stages," Fu said.

Explore further: Device might detect breast cancer-linked swelling sooner

More information: Mei R. Fu et al, Machine learning for detection of lymphedema among breast cancer survivors, mHealth (2018). DOI: 10.21037/mhealth.2018.04.02

Related Stories

Device might detect breast cancer-linked swelling sooner

May 3, 2018
(HealthDay)—Testing for small changes in the flow of lymph fluids after breast cancer surgery can spot the start of a painful swelling known as lymphedema before it becomes hard to treat, a new study suggests.

Study identifies method for detecting latent stage of lymphedema

December 18, 2015
Many are aware that hair loss is a common side effect associated with chemotherapy. However, another albeit common late side effect of cancer treatment is the abnormal swelling of one or more limbs, known as lymphedema. Lymphedema ...

Researchers study patients' genetic and susceptibility risk factors for lymphedema

February 8, 2017
Each year, about 1.38 million women worldwide are diagnosed with breast cancer. Advances in diagnosis and treatment have facilitated a 90-percent, five-year survival rate, among those treated. However, with the increased ...

Researchers find a new solution in detecting breast-cancer related lymphedem

November 12, 2013
Viewed as one of the most feared outcomes of breast cancer treatment, doctors struggle detecting and diagnosing breast-cancer related Lymphedema—a condition affecting the lymphatic system and causing psychosocial distress ...

Researchers conduct study with innovative tools to help early identification and treatment of lymphedema

September 30, 2016
Each year, about 1.38 million women worldwide are diagnosed with breast cancer. Advances in diagnosis and treatment have facilitated a 90 percent, five-year survival rate, among those treated. Given the increased rate and ...

Recommended for you

Immunotherapy combo not approved for advanced kidney cancer patients on the NHS

December 14, 2018
People with a certain type of advanced kidney cancer will not be able to have a combination of two immunotherapy drugs on the NHS in England.

New drug seeks receptors in sarcoma cells, attacks tumors in animal trials

December 13, 2018
A new compound that targets a receptor within sarcoma cancer cells shrank tumors and hampered their ability to spread in mice and pigs, a study from researchers at the University of Illinois reports.

Surgery unnecessary for many prostate cancer patients

December 13, 2018
Otherwise healthy men with advanced prostate cancer may benefit greatly from surgery, but many with this diagnosis have no need for it. These conclusions were reached by researchers after following a large group of Scandinavian ...

Combining three treatment strategies may significantly improve melanoma treatment

December 12, 2018
A study by a team led by a Massachusetts General Hospital (MGH) investigator finds evidence that combining three advanced treatment strategies for malignant melanoma—molecular targeted therapy, immune checkpoint blockade ...

Researchers use computer model to predict prostate cancer progression

December 12, 2018
An international team of cancer researchers from Denmark and Germany have used cancer patient data to develop a computer model that can predict the progression of prostate cancer. The model is currently being implemented ...

New insight into stem cell behaviour highlights therapeutic target for cancer treatment

December 12, 2018
Research led by the University of Plymouth and Technische Universität Dresden has identified a new therapeutic target for cancer treatment and tissue regeneration – a protein called Prominin-1.

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.