Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

Abstract

Purpose: Radiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.

Methods and materials: One hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I_M) and erythema (I_E) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient's dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.

Results: Thirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I_M,T0 and I_E,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with I_M,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.

Conclusions: Spectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life.

Keywords: breast; machine learning; radiation oncology; spectrophotometry; toxicity.

Figure 1

Schematic workflow and methodology of the study.

Figure 2

Measured values of melanin (green) and erythema (orange) indexes before (T0) and after different times (T1, T2 and T3) after radiotherapy. Data are reported as box-and-whisker plots.

Figure 3

Box-and-whisker plots for I_M,T0, I_E,T0 and the three continuous variables (BMI, PTV1 and PTV2) associated with RTOG toxicity classification at univariate analysis.

Figure 4

Receiver operating characteristic (ROC) curve for the different machine learning models: (A) Logistic Regression, (B) Support Vector Machine with linear kernel, (C) Support Vector Machine with power kernel, (D) Support Vector Machine with RBF kernel, (E) Support Vector Machine with sigmoid kernel and (F) Classification and Regression Tree.

Figure 5

Classification and Regression tree analysis (CART) for the most significant variables.