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

SUBMITTER: Cilla S 

PROVIDER: S-EPMC9853396 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature