External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort

Abstract

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 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.

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

“…A recently published logistic regression model for acute breast desquamation after adjuvant external beam breast radiation therapy developed in 3 combined external breast radiation therapy cohorts failed to validate in the multicenter REQUITE cohort. 24 The aim of this study was to use ML algorithms to develop and optimise a prediction model for acute desquamation in the REQUITE breast cancer cohort . ML techniques have previously been used to predict acute skin toxicity during breast radiation therapy.…”

“…To that extent, a logistic regression model for acute breast desquamation after external beam radiation therapy (EBRT) recently developed in 3 combined radiation therapy cohorts failed to validate externally in the multicenter international REQUITE cohort. 24 Therefore, the aim of this study was to use ML algorithms to develop and optimise a prediction model for acute breast desquamation after EBRT in the REQUITE breast cancer cohort.…”

Dose-volume predictors of radiation dermatitis in patients undergoing radiotherapy after radical mastectomy for breast cancer

“…In previous studies, diabetes mellitus and smoking were also associated with the incidence and severity of ARD. [18,27] None of the patients included in the present study had a history of smoking or diabetes mellitus.…”

Predicting acute radiation dermatitis in breast cancer: a prospective cohort study

“…Large breasts and obesity have been found to increase the risk of ARD according to multiple observational studies [ 26 ]. BMI is closely related to breast volume [ 27 ].…”