Developing and validating a prediction model for lymphedema detection in breast cancer survivors

Abstract

Purpose: Early detection and intervention of lymphedema is essential for improving the quality of life of breast cancer survivors. Previous studies have shown that patients have symptoms such as arm tightness and arm heaviness before experiencing obvious limb swelling. Thus, this study aimed to develop a symptom-warning model for the early detection of breast cancer-related lymphedema.

Methods: A cross-sectional study was conducted at a tertiary hospital in Beijing between April 2017 and December 2018. A total of 24 lymphedema-associated symptoms were identified as candidate predictors. Circumferential measurements were used to diagnose lymphedema. The data were randomly split into training and validation sets with a 7:3 ratio to derive and evaluate six machine learning models. Both the discrimination and calibration of each model were assessed on the validation set.

Results: A total of 533 patients were included in the study. The logistic regression model showed the best performance for early detection of lymphedema, with AUC = 0.889 (0.840-0.938), sensitivity = 0.771, specificity = 0.883, accuracy = 0.825, and Brier scores = 0.141. Calibration was also acceptable. It has been deployed as an open-access web application, allowing users to estimate the probability of lymphedema individually in real time. The application can be found at https://apredictiontoolforlymphedema.shinyapps.io/dynnomapp/.

Conclusion: The symptom-warning model developed by logistic regression performed well in the early detection of lymphedema. Integrating this model into an open-access web application is beneficial to patients and healthcare providers to monitor lymphedema status in real-time.

Keywords: Breast cancer; Early detection; Lymphedema; Machine learning; Prediction model; Real-time monitoring; Symptom.