Artificial Intelligence for Prediction of Breast Cancer-Related Lymphedema: A Systematic Review.

Authors

Hatan Mortada, Mustafa Qais Muhsin Al-Khafaji, Zainalabden E Jefri, Faris Sultan, Saad Alrobaiea, Feras Alshomer

Published in

Plastic and reconstructive surgery. Global open. Volume 14. Issue 10. Pages e8132. Epub Oct 06, 2026.

Abstract

Breast cancer-related lymphedema (BCRL) impairs quality of life and function, and early detection is crucial for preventive intervention. AI models, particularly machine learning and deep learning, hold promise for improving BCRL prediction and personalized risk stratification. This systematic review evaluates AI applications for predicting BCRL and, where available, summarizes how their reported performance compared with traditional diagnostic or non-AI methods.
This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search in MEDLINE, EMBASE, Cochrane Library, and Google Scholar was conducted in July 2024 using keywords related to breast cancer, lymphedema, and AI. Relevant studies were screened and selected based on predefined inclusion and exclusion criteria, with performance metrics such as accuracy, sensitivity, and specificity extracted for analysis.
Seven studies from 2018 to 2024 were included, with 5284 patients. Among the 5 studies reporting BCRL classification performance, AI models achieved accuracies of 81%-93.75% (mean 87.2%), mean sensitivity 85.5%, and mean specificity 87.6%. Commonly reported predictors included higher body mass index, greater lymph node burden, and receipt of chemotherapy or radiotherapy. Younger age and neoadjuvant chemotherapy were associated with higher predicted risk in some studies.
AI shows promise for early detection and risk stratification in BCRL, but current models remain exploratory rather than clinically established. Small sample sizes, data heterogeneity, absent external validation, and inconsistent calibration highlight the need for larger, multi-institutional datasets, standardized evaluation, and prospective validation before clinical integration.

PMID:
42840800
Bibliographic data and abstract were imported from PubMed on 07 Oct 2026.

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