ArticleJournal of surgical oncology2025
Optimizing Lymphedema Management After Breast Cancer: Predictive Risk Models in Clinical Practice.
Article in Journal of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Comprehensive overview of management and risk assessment of breast cancer-related lymphedema: a multidisciplinary approach.Frontiers in oncology · 2026Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesLymphedema secondary to multimodal breast cancer treatment is a relatively common complication that significantly impacts patients' quality of life. Despite identifying several associated risk factors, accurately assessing individual risk remains challenging. This study aims to develop predictive tools integrating patient characteristics, tumor attributes, and treatment modalities to optimize clinical surveillance, enhance prevention, and enable earlier diagnosis.
methodsData were analyzed from 309 patients referred to the Lymphedema Unit of Rehabilitation Service who underwent lymphadenectomy for breast cancer between January 2016 and December 2021. Collected variables included patient demographics, tumor clinicopathological features, and treatment details. A lymphedema incidence study was conducted, complemented by univariate and multivariate regression analyses to identify risk factors. A nomogram was developed to predict high-risk patients, facilitating personalized prevention and management strategies.
resultsThe cumulative incidence of lymphedema was 18.4%. Independent risk factors included high body mass index, sedentary lifestyle, number of positive nodes (N stage), and radiotherapy, particularly targeting the breast, axilla, and supra-infraclavicular regions. The logistic regression model demonstrated an area under the ROC curve (AUC) of 0.75, with acceptable calibration, validating the predictive model.
conclusionsThe predictive tools developed provide healthcare professionals with a means to identify patients at elevated risk of lymphedema, supporting individualized prevention and management.
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