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Development and validation of a risk prediction model for breast cancer-related lymphedema in postoperative Chinese patients with breast cancer

Published:December 31, 2022DOI:https://doi.org/10.1016/j.ejon.2022.102258

      Highlights

      • Predictors of lymphoedema risk included time since breast cancer surgery, level of lymph node dissection, number of lymph nodes dissected, radiotherapy, and postoperative BMI.
      • These results have potential value in guiding strategies for early monitoring of lymphoedema and patient education in clinical practice.
      • We constructed a risk factor nomogram, combining clinical and therapeutic characteristics, in this study. The model will be helpful for the identification and classification of BCRL in cancer patients.

      Abstract

      Objective

      Breast cancer-related lymphedema (BCRL) is a common post-operative complication in patients with breast cancer. Here, we sought to develop and validate a predictive model of BCRL in Chinese patients with breast cancer.

      Methods

      Clinical and demographic data on patients with breast cancer were collected between 2016 and 2021 at a Cancer Hospital in China. A nomogram for predicting the risk of lymphedema in postoperative patients with breast cancer was constructed and verified using R 3.5.2 software. Model performance was evaluated using area under the ROC curve (AUC) and goodness-of-fit statistics, and the model was internally validated.

      Results

      A total of 1732 postoperative patients with breast cancer, comprising 1212 and 520 patients in the development and validation groups, respectively, were included. Of these 438 (25.39%) developed lymphedema. Significant predictors identified in the predictive model were time since breast cancer surgery, level of lymph node dissection, number of lymph nodes dissected, radiotherapy, and postoperative body mass index. At the 31.9% optimal cut-off the model had AUC values of 0.728 and 0.710 in the development and validation groups, respectively. Calibration plots showed a good match between predicted and observed rates. In decision curve analysis, the net benefit of the model was better between threshold probabilities of 10%–80%.

      Conclusion

      The model has good discrimination and accuracy for lymphedema risk assessment, which can provide a reference for individualized clinical prediction of the risk of BCRL. Multicenter prospective trials are required to verify the predictive value of the model.

      Keywords

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