Early identification of low-grade acute radiation dermatitis using in vivo optical coherence tomography (OCT) images of human head and neck

Radiation therapy remains an essential component of cancer treatment, with nearly 50% of cancer patients receiving radiation therapy at some point during the course of their illness. Of those, the vast majority experience some form of acute radiation dermatitis ( ARD) or radiation-induced skin injury. ARD results in significant discomfort, restriction of daily activities, overall decrease in the quality of life and even cessation of the necessary radiation therapy with detrimental survival effects. Unfortunately, research into the causes and possible management strategies for ARD is hindered by the lack of biomarkers for the quantitative assessment of the early changes associated with the condition. This study provides the basis to identify low grade ARD, using Optical Coherence Tomography (OCT) images with the extraction of conventional image intensity and novel features. Twenty-two patients were imaged twice each week until the end of their radiation treatment resulting in 1487 images. The severity of the cases was graded by an expert oncologist. The preliminary results of various machine learning (ML) classifiers have shown that an LDA based approach provided the best performance, separating normal skin from very early ARD, with an accuracy of 85%.