External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort - Rattay, Tim; Seibold, Petra; Aguado-Barrera, Miguel E.; Altabas, Manuel; Azria, David; Barnett, Gillian C.; Bultijnck, Renée; Chang-Claude, Jenny; Choudhury, Ananya; Coles, Charlotte E.; Dunning, Alison M.; Elliott, Rebecca M.; et al. (Frontiers in Oncology, 2020)
Background 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 then externally validate the models in patients recruited into the prospective multi-centre 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 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. The model validated in REQUITE with good discrimination (AUC 0.65) though moderate 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 for acute erythema following radiotherapy after breast-conserving surgery. This model retained its moderate discriminatory power but will benefit from further re-calibration. Future improvements and more accurate predictions are expected through the addition of genetic markers and application of other modelling and machine learning techniques.