Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry - Cilla, Savino; Romano, Carmela; Macchia, Gabriella; Boccardi, Mariangela; Pezzulla, Donato; Buwenge, Milly; Castelnuovo, Augusto Di; Bracone, Francesca; Curtis, Amalia De; Cerletti, Chiara; Iacoviello, Licia; Donati, Maria Benedetta; et al. (Frontiers in Oncology, 2023)
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 (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4 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 split into a training and validation set used for model development and cross-validation (75%/25% split). Three supervised machine learning models, including logistic regression (LR), support vector machine (SVM) and classification and regression tree analysis (CART) were employed for modeling and skin 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 (IM,T0 and IE,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). Classification performances reported precision, recall and F1-values greater than 0.8 for all models. CART analysis classified patients with IM,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; PTV1 and PTV2 volumes subsequently played a significant role in increasing the classification rate among the patients. 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.