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<title>Optimizing Lymphedema Management After Breast Cancer: Predictive Risk Models in Clinical Practice</title>
<creator>Cano Lallave, Enrique</creator>
<creator>Frutos Bernal, Elisa</creator>
<creator>Anciones Polo, María del Dulce Nombre</creator>
<creator>Serrano Sánchez, Esther</creator>
<creator>Rodríguez Guerrero, Ian</creator>
<creator>Cuenda Gamboa, Paula</creator>
<creator>Muñoz Bellvis, Luis</creator>
<creator>Eguía Larrea, Marta</creator>
<subject>Breast cancer</subject>
<subject>Lymphadenectomy</subject>
<subject>Lymphedema</subject>
<subject>Predictive tools</subject>
<subject>Risk factors</subject>
<description>[EN]Background and Objectives: Lymphedema secondary to multimodal breast cancer treatment is a relatively common complication&#xd;
that significantly impacts patients' quality of life. Despite identifying several associated risk factors, accurately&#xd;
assessing individual risk remains challenging. This study aims to develop predictive tools integrating patient characteristics,&#xd;
tumor attributes, and treatment modalities to optimize clinical surveillance, enhance prevention, and enable earlier diagnosis.&#xd;
Methods: Data were analyzed from 309 patients referred to the Lymphedema Unit of Rehabilitation Service who underwent&#xd;
lymphadenectomy for breast cancer between January 2016 and December 2021. Collected variables included patient demographics,&#xd;
tumor clinicopathological features, and treatment details. A lymphedema incidence study was conducted, complemented&#xd;
by univariate and multivariate regression analyses to identify risk factors. A nomogram was developed to predict&#xd;
high‐risk patients, facilitating personalized prevention and management strategies.&#xd;
Results: The cumulative incidence of lymphedema was 18.4%. Independent risk factors included high body mass index,&#xd;
sedentary lifestyle, number of positive nodes (N stage), and radiotherapy, particularly targeting the breast, axilla, and&#xd;
supra‐infraclavicular regions. The logistic regression model demonstrated an area under the ROC curve (AUC) of 0.75, with&#xd;
acceptable calibration, validating the predictive model.&#xd;
Conclusions: The predictive tools developed provide healthcare professionals with a means to identify patients at elevated risk&#xd;
of lymphedema, supporting individualized prevention and management.</description>
<date>2026-01-23</date>
<date>2026-01-23</date>
<date>2025-05-13</date>
<type>info:eu-repo/semantics/article</type>
<identifier>Cano-Lallave, E., Frutos-Bernal, E., Anciones-Polo, M., Serrano-Sánchez, E., Rodríguez-Guerrero, I., Cuenda-Gamboa, P., Muñoz-Bellvis, L. and Eguía-Larrea, M. (2025), Optimizing Lymphedema Management After Breast Cancer: Predictive Risk Models in Clinical Practice. Journal of Surgical Oncology, 131: 1628-1636. https://doi.org/10.1002/jso.28146</identifier>
<identifier>0022-4790</identifier>
<identifier>http://hdl.handle.net/10366/169229</identifier>
<identifier>10.1002/JSO.28146</identifier>
<identifier>1096-9098</identifier>
<language>eng</language>
<relation>https://doi.org/10.1002/jso.28146</relation>
<rights>http://creativecommons.org/publicdomain/zero/1.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>CC0 1.0 Universal</rights>
<publisher>Wiley</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>