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Llorián-Salvador, O.* ; Windeler, N.* ; Martin, N.* ; Etzel, L.* ; Andrade-Navarro, M.A.* ; Bernhardt, D. ; Rost, B.* ; Borm, K.J.* ; Combs, S.E. ; Duma, M.N.* ; Peeken, J.C.

CT-based radiomics for predicting breast cancer radiotherapy side effects.

Sci. Rep. 14:20051 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Skin inflammation with the potential sequel of moist epitheliolysis and edema constitute the most frequent breast radiotherapy (RT) acute side effects. The aim of this study was to compare the predictive value of tissue-derived radiomics features to the total breast volume (TBV) for the moist cells epitheliolysis as a surrogate for skin inflammation, and edema. Radiomics features were extracted from computed tomography (CT) scans of 252 breast cancer patients from two volumes of interest: TBV and glandular tissue (GT). Machine learning classifiers were trained on radiomics and clinical features, which were evaluated for both side effects. The best radiomics model was a least absolute shrinkage and selection operator (LASSO) classifier, using TBV features, predicting moist cells epitheliolysis, achieving an area under the receiver operating characteristic (AUROC) of 0.74. This was comparable to TBV breast volume (AUROC of 0.75). Combined models of radiomics and clinical features did not improve performance. Exclusion of volume-correlated features slightly reduced the predictive performance (AUROC 0.71). We could demonstrate the general propensity of planning CT-based radiomics models to predict breast RT-dependent side effects. Mammary tissue was more predictive than glandular tissue. The radiomics features performance was influenced by their high correlation to TBV volume.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Breast Cancer ; Computed Tomography ; Edema ; Machine Learning ; Moist Cells Epitheliolysis ; Radiomics ; Radiotherapy ; Side Effects ; Skin Inflammation
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Journal Scientific Reports
Quellenangaben Volume: 14, Issue: 1, Pages: , Article Number: 20051 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Medicine (IRM)