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Updated: Jul 31, 2025
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
Published on: February 6, 2020
Risk prediction models for breast cancer-related lymphedema: A systematic review and meta-analysis
Aomei Shen1, Xiaoxia Wei2, Fei Zhu3
1Department of Nursing, Tianjin Medical University Cancer Institute & Hospital, Huanhuxi Road, Hexi District, Tianjin, China; Division of Medical & Surgical Nursing, School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing, China; Peking University Health Science Centre for Evidence-Based Nursing: A Joanna Briggs Institute Affiliated Group, Beijing, China.
Purpose:
To review and critically evaluate currently available risk prediction models for breast cancer-related lymphedema (BCRL).
Methods:
PubMed, Embase, CINAHL, Scopus, Web of Science, the Cochrane Library, CNKI, SinoMed, WangFang Data, VIP Database were searched from inception to April 1, 2022, and updated on November 8, 2022. Study selection, data extraction and quality assessment were conducted by two independent reviewers. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability. Meta-analysis of AUC values of model external validations was performed using Stata 17.0.
Results:
Twenty-one studies were included, reporting twenty-two prediction models, with the AUC or C-index ranging from 0.601 to 0.965. Only two models were externally validated, with the pooled AUC of 0.70 (n = 3, 95%CI: 0.67 to 0.74), and 0.80 (n = 3, 95%CI: 0.75 to 0.86), respectively. Most models were developed using classical regression methods, with two studies using machine learning. Predictors most frequently used in included models were radiotherapy, body mass index before surgery, number of lymph nodes dissected, and chemotherapy. All studies were judged as high overall risk of bias and poorly reported.
Conclusions:
Current models for predicting BCRL showed moderate to good predictive performance. However, all models were at high risk of bias and poorly reported, and their performance is probably optimistic. None of these models is suitable for recommendation in clinical practice. Future research should focus on validating, optimizing, or developing new models in well-designed and reported studies, following the methodology guidance and reporting guidelines.
