Development and validation of a predictive risk model for acute skin toxicity in patients undergoing breast radiotherapy

Background
Clinically significant side-effects from radiotherapy affect around a quarter of breast cancer patients and may have a considerable impact on breast cosmesis and quality of life. If patients at high risk of radiation toxicity could be identified at breast cancer diagnosis, this could be taken into account when discussing treatment options. The aim of this study was to develop a predictive model for acute skin toxicity in patients undergoing breast radiotherapy.

Methods
Using multivariate logistic regression and backwards elimination, the risk model for acute skin toxicity (moderately brisk reaction and/or ≥1 acute desquamation) was first developed in patient cohorts treated by breast-conserving surgery and whole breast radiotherapy in three European centres (Leicester, Heidelberg/Mannheim, Cambridge; total n=2,012) with a biologically effective dose (BED) range from 47.1 to 67.2 Gy. It was externally validated in breast cancer patients enrolled in the multi-centre REQUITE cohort study (n=2,062; BED range 44.6 to 75.4 Gy).

Results
The final model with the variables age, BED, cup size or BMI, and presence/absence of diabetes, smoking, and hypertension proved to give best prediction of acute skin toxicity with a c-statistic (AUC) of 0.79 in the development and 0.75 in the validation cohort and was well calibrated (Hosmer-Lemeshow p=0.53).

Conclusions
A predictive model for radiotoxicity has the potential to give clinicians important information when planning treatment to reduce side-effects and optimise quality of life. The addition of prognostic genetic markers investigated as part of the REQUITE study is likely to improve model performance. Similar models can be developed for other toxicity endpoints, such as breast fibrosis, and should also be validated for patients undergoing chest wall radiotherapy and breast reconstruction.