Researchers Use AI to Learn More about Cancer Symptom Clusters

February 21, 2019
Researchers Use AI to Learn More about Cancer Symptom Clusters
In the first study of its kind, researchers from the University of Surrey and the University of California used Network Analysis (NA) to examine the structure and relationships between 38 common symptoms reported by over 1300 cancer patients receiving chemotherapy. [National Cancer Institute]

In the first study of its kind, researchers from the University of Surrey and the University of California used Network Analysis (NA) to examine the structure and relationships between 38 common symptoms reported by over 1300 cancer patients receiving chemotherapy.

NA is capable of identifying and predicting the development of different combinations of symptoms or clusters. The researchers aim to examine how one symptom cluster is associated with other symptom clusters, with the goal of helping alleviate much of the distress caused by their occurrence and severity in oncology patients, who on average experience 15 unrelieved symptoms.

“This is the first use of Network Analysis as a method of examining the relationships between common symptoms suffered by a large group of cancer patients undergoing chemotherapy. The detailed and intricate analysis this method provides could become crucial in planning the treatment of future patients—helping to better manage their symptoms across their healthcare journey,” noted Payam Barnaghi, PhD, professor of machine intelligence at the University of Surrey, and co-first author on the study.

The team’s findings are published in an article titled “Network Analysis of the Multidimensional Symptom Experience of Oncology” in Scientific Reports.

The authors wrote that, “while progress is being made in symptom clusters research, one of the major gaps in knowledge using standard statistical approaches is that the nature of the relationships among individual symptoms and symptom clusters has not been evaluated. This gap in knowledge prevents the identification of key symptom(s) that exert an influence on other co-occurring symptoms or symptom clusters that may be potential target(s) for therapeutic interventions.”

Some of the most common symptoms reported by patients were nausea, difficulty concentrating, fatigue, drowsiness, dry mouth, hot flushes, numbness, and nervousness. The team then grouped these symptoms into three key networks—occurrence, severity, and distress. The team used two different models of Pairwise Markov Random Fields (PMRF) to examine the nature and structure of interactions for three different dimensions of patients’ symptom experience. The NA allowed the team to identify nausea as central—impacting symptoms across all three different key networks.

The team’s findings provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. The authors noted that, “based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.”

The authors wrote that, “in terms of the occurrence network, nausea had the highest scores for all three centrality indices. In this sample, 47.48% of patients reported nausea prior to their next dose of chemotherapy.” Although vomiting can be well controlled, nausea remains a persistent symptom that can compromise a patient’s nutritional status, result in distress, decreases quality of life, and can even lead to the discontinuation of cancer treatment.

While the NA of cross-sectional data does not demonstrate causality, it does provide some insights into the structural importance of each of the symptoms within each of the networks. “This fresh approach will allow us to develop and test novel and more targeted interventions to decrease symptom burden in cancer patients undergoing chemotherapy,” noted Christine Miaskowski, RN, PhD, FAAN, professor in the department of physiological nursing, at the University of California, San Francisco.