Prediction of breast cancer-related lymphedema risk after postoperative radiotherapy via multivariable logistic regression analysis

PURPOSE: We identified novel clinical and dosimetric prognostic factors affecting breast cancer-related lymphedema after postoperative radiotherapy (RT) and developed a multivariable logistic regression model to predict lymphedema in these patients. METHODS AND MATERIALS: In total, 580 patients with unilateral breast cancer were retrospectively reviewed. All patients underwent breast surgery and postoperative RT with or without systemic treatment in 2015. Among the 580 patients, 532 with available RT plan data were randomly divided into training (n=372) and test (n=160) cohorts at a 7:3 ratio to generate and validate the lymphedema prediction models, respectively. An area under the curve (AUC) value was estimated to compare models. RESULTS: The median follow-up duration was 5.4 years. In total, 104 (17.9%) patients experienced lymphedema with a cumulative incidence as follows: 1 year, 10.5%; 3 years, 16.4%; and 5 years, 17.6%. Multivariate analysis showed that body mass index ≥25 kg/m(2) (hazard ratio [HR] 1.845), dissected lymph nodes ≥7 (HR 1.789), and taxane-base chemotherapy (HR 4.200) were significantly associated with increased lymphedema risk. Conversely, receipt of RT at least 1 month after surgery reduced the risk of lymphedema (HR 0.638). A multivariable logistic regression model using the above factors, as well as the minimum dose of axillary level I and supraclavicular lymph node, was created with an AUC of 0.761 and 0.794 in the training and test cohorts, respectively. CONCLUSIONS: Our study demonstrated that a shorter interval from surgery to RT and other established clinical factors were associated with increased lymphedema risk. By combining these factors with two dosimetric parameters, we propose a multivariable logistic regression model for breast cancer-related lymphedema prediction after RT.