Breast composition and dose deposition to fat and fibroglandular tissues are associated with breast side effects after radiation therapy
Year: 2026
Type: article
Abstract: OBJECTIVE: Breast comprises different tissues with potentially different dose responses to radiation therapy (RT). This study investigates the correlation between RT dose, breast composition, and side effects from breast RT. MATERIAL/METHODS: Data from 922 early-stage breast cancer patients who underwent breast-conserving surgery and RT from the REQUITE study were included. Breast pain, oedema, atrophy, and induration were assessed immediately post-RT, one-year, and two-years post-RT. Maximum severity scores for each toxicity were used for analysis. Breast tissue was divided into "fat" and "fibroglandular" substructures from computed tomography (CT) using a Gaussian Mixture Model. The correlation between breast characteristics, toxicity, dosimetric parameters, and patient and clinical variables was investigated using ordinal regression. The model's fit was evaluated using the Akaike Information Criterion in SPSS v.29. RESULTS: Breast volume and breast density were associated with increased risk of breast oedema, atrophy, and induration in multivariable analysis (p<0.05). Higher mean dose and dose uniformity were observed for fibroglandular compared to fatty tissue at all severity levels, while there was no significant difference in the maximum dose to either substructure. Higher dose deposit to fat was associated with breast pain and oedema, while breast atrophy and induration were associated with dose to fibroglandular tissue. All best-performing toxicity models included dosimetric parameters derived from breast composition. CONCLUSION: Breast characterisation offers new insight into the link between dose and toxicity. Breast density and dose parameters from different substructures were associated with different breast toxicity. These findings further support the importance of dose homogeneity of breast RT planning.
Source: The Breast
Authors: Tanwiwat Jaikuna, Fiona Wilson, Carmel Anandadas, David Azria, Jenny Chang-Claude, Maria Carmen De Santis, Sara Gutiérrez-Enríquez, Marcel van Herk, Peter Hoskin, Lea Kotzki, Maarten Lambrecht, Zoe Lingard, Petra Seibold, Alejandro Seoane, Elena Sperk, R Paul Symonds, Christopher J. Talbot, Tiziana Rancati, Tim Rattay, Victoria Reyes, Barry S. Rosenstein, Dirk de Ruysscher, Ana Vega, Liv Veldeman, Adam Webb, Catharine M.L. West, Eliana Vasquez Osorio, Marianne C. Aznar
Institutions: Siriraj Hospital, Mahidol University, University of Manchester, The Christie NHS Foundation Trust, The Christie Hospital, Inserm, Université de Montpellier, Institut de Recherche en Cancérologie de Montpellier, German Cancer Research Center, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, Fondazione IRCCS Istituto Nazionale dei Tumori, Vall d'Hebron Institute of Oncology, KU Leuven, Vall d'Hebron Hospital Universitari, Heidelberg University, Medizinische Fakultät Mannheim, University of Leicester, Icahn School of Medicine at Mount Sinai, Maastricht University Medical Centre, Maastricht University, Maastro Clinic, Centre for Biomedical Network Research on Rare Diseases, Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago, Ghent University Hospital
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,200
Funders: National Institute on Handicapped Research, Fundación Cellex, Deutsches Krebsforschungszentrum, Centres de Recerca de Catalunya, Cancer Research UK, National Institute for Health and Care Research, European Commission, Bundesministerium für Bildung und Forschung, Generalitat de Catalunya, Cancer Research UK Manchester Centre, Seventh Framework Programme, Manchester Biomedical Research Centre, Engineering and Physical Sciences Research Council, Agencia Estatal de Investigación, NIHR Leicester Biomedical Research Centre, FP7 Coordination of Research Activities
Awards: G1175115578, Big data for small patients - Building "child-size" individual predictive models for life after childhood cancer, G1483213741, G168623166, G1798610363, G2064873908, G2213711621, G2262748287, G2316486796, G2462415295, G2601397997, G2728518417, G2856535329, G2881562199, G3005324705, G300979063, RADprecise - Personalisierte Radiotherapie: Einbeziehung der zellulären Reaktion auf Bestrahlung in die personalisierte Behandlungsplanung zur Minimierung der Radiotherapie-Nebenwirkungen, G3480869486, G3546882995, G3681454997, G4042783231, G4235863616, G425668148, G428810563, G4291948961, G4630167721, G4731613563, G4869084219, G492498531, G5080475149, G5195188938, Validating predictive models of radiotherapy toxicity to improve quality-of-life and reduce side-effects in cancer survivors, G6200043914, G62261614, G6504970064, G6656126374, G7084143925, G8228929752, G8807413798
OpenAlex ID: W7118594845