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000165985 1001_ $$aRattay, Tim$$b0
000165985 245__ $$aExternal Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort.
000165985 260__ $$aLausanne$$bFrontiers Media$$c2020
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000165985 520__ $$aBackground: Acute skin toxicity is a common and usually transient side-effect of breast radiotherapy although, if sufficiently severe, it can affect breast cosmesis, aftercare costs and the patient's quality-of-life. The aim of this study was to develop predictive models for acute skin toxicity using published risk factors and externally validate the models in patients recruited into the prospective multi-center REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve QUalITy of lifE in cancer survivors) study. Methods: Patient and treatment-related risk factors significantly associated with acute breast radiation toxicity on multivariate analysis were identified in the literature. These predictors were used to develop risk models for acute erythema and acute desquamation (skin loss) in three Radiogenomics Consortium cohorts of patients treated by breast-conserving surgery and whole breast external beam radiotherapy (n = 2,031). The models were externally validated in the REQUITE breast cancer cohort (n = 2,057). Results: The final risk model for acute erythema included BMI, breast size, hypo-fractionation, boost, tamoxifen use and smoking status. This model was validated in REQUITE with moderate discrimination (AUC 0.65), calibration and agreement between predicted and observed toxicity (Brier score 0.17). The risk model for acute desquamation, excluding the predictor tamoxifen use, failed to validate in the REQUITE cohort. Conclusions: While most published prediction research in the field has focused on model development, this study reports successful external validation of a predictive model using clinical risk factors for acute erythema following radiotherapy after breast-conserving surgery. This model retained discriminatory power but will benefit from further re-calibration. A similar model to predict acute desquamation failed to validate in the REQUITE cohort. Future improvements and more accurate predictions are expected through the addition of genetic markers and application of other modeling and machine learning techniques.
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000165985 7001_ $$aAguado-Barrera, Miguel E$$b2
000165985 7001_ $$aAltabas, Manuel$$b3
000165985 7001_ $$aAzria, David$$b4
000165985 7001_ $$aBarnett, Gillian C$$b5
000165985 7001_ $$aBultijnck, Renée$$b6
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000165985 7001_ $$aPost, Giselle$$b16
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000165985 7001_ $$aReyes, Victoria$$b18
000165985 7001_ $$aRosenstein, Barry S$$b19
000165985 7001_ $$aDe Ruysscher, Dirk$$b20
000165985 7001_ $$ade Santis, Maria C$$b21
000165985 7001_ $$aSperk, Elena$$b22
000165985 7001_ $$aStobart, Hilary$$b23
000165985 7001_ $$aSymonds, R Paul$$b24
000165985 7001_ $$aTaboada-Valladares, Begoña$$b25
000165985 7001_ $$aVega, Ana$$b26
000165985 7001_ $$aVeldeman, Liv$$b27
000165985 7001_ $$aWebb, Adam J$$b28
000165985 7001_ $$aWest, Catharine M$$b29
000165985 7001_ $$aValdagni, Riccardo$$b30
000165985 7001_ $$aTalbot, Christopher J$$b31
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000165985 7001_ $$aLievens, Yolande$$b33
000165985 7001_ $$avan Eijkeren, Marc$$b34
000165985 7001_ $$aOst, Piet$$b35
000165985 7001_ $$aFonteyne, Valérie$$b36
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000165985 7001_ $$aBlaschke, Thomas$$b50
000165985 7001_ $$aWeiß, Christian$$b51
000165985 7001_ $$aHelmbold, Irmgard$$b52
000165985 7001_ $$aWeißenberger, Christian$$b53
000165985 7001_ $$aStegmaier, Petra$$b54
000165985 7001_ $$aClaßen, Johannes$$b55
000165985 7001_ $$aGiesche, Ulrich$$b56
000165985 7001_ $$aSautter-Bihl, Marie-Luise$$b57
000165985 7001_ $$aNeu, Burkhard$$b58
000165985 7001_ $$aSchnabel, Thomas$$b59
000165985 7001_ $$aEhmann, Michael$$b60
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