The potential of thermal imaging as an early predictive biomarker of radiation dermatitis during radiotherapy for head and neck cancer: a prospective study.

ABSTRACT:

Background

Predicting radiation dermatitis (RD), a common radiotherapy toxicity, is essential for clinical decision-making regarding toxicity management. This prospective study aimed to develop and validate a machine-learning model to predict the occurrence of grade ≥ 2 RD using thermal imaging in the early stages of radiotherapy in head and neck cancer.

Methods

Thermal images of neck skin surfaces were acquired weekly during radiotherapy. A total of 202 thermal images were used to calculate the difference map of neck skin temperature and analyze to extract thermal imaging features. Changes in imaging features during treatment were assessed in the two RD groups, grade ≥ 2 and grade ≤ 1 RD, classified according to the Common Terminology Criteria for Adverse Events (CTCAE) gui

SUBMITTER: Park YI 

PROVIDER: S-EPMC11844184 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature