A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Park, Y.I.; Choi, S.H.; Hong, C.-S.; Cho, M.-S.; Son, J.; Han, M.C.; Kim, J.; Kim, H.; Kim, D.W.; Kim, J.S.
• Is Part Of: Clinical oncology. Volume 35:Issue 1(2023)
• Page Start: e10
• Page End: e19
• Publication Date: 2023-01
• Subjects: Convolutional neural networks -- dermatitis grading scale -- radiation dermatitis -- radiation therapy -- skin toxicity -- skin-dose distribution; Oncology -- Periodicals; Tumors -- Periodicals; Cancer -- Treatment -- Periodicals; Radiotherapy -- Periodicals; Neoplasms -- Periodicals; Cancer -- Radiotherapy; Cancer -- Treatment; Oncology; Medical radiology; Radiotherapy; Tumors; Electronic journals; Periodicals; 616.994
• DOI: 10.1016/j.clon.2022.07.001
• Languages: English
• ISSNs: 0936-6555

Abstract

Abstract: Aims: Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel radiation dermatitis segmentation system based on convolutional neural networks (CNNs) to consistently evaluate radiation dermatitis. Materials and methods: The radiation dermatitis segmentation system is designed to segment the radiation dermatitis occurrence area using skin photographs and skin-dose distribution. A CNN architecture with a dilated convolution layer and skip connection was designed to estimate the radiation dermatitis area. Seventy-three skin photographs obtained from patients undergoing radiotherapy were collected for training and testing. The ground truth of radiation dermatitis segmentation is manually delineated from the skin photograph by an experienced radiation oncologist and medical physicist. We converted the skin photographs to RGB (red-green-blue) and CIELAB (lightness (L ∗ ), red-green (a ∗ ) and blue-yellow (b ∗ )) colour information and trained the network to segment faint and severe radiation dermatitis using three different input combinations: RGB, RGB + CIELAB (RGBLAB) and RGB + CIELAB + skin-dose distribution (RGBLAB_D). The proposed system was evaluated using the Dice similarity coefficient (DSC), sensitivity, specificity and normalised Matthews correlation coefficient (nMCC). A paired t -test was used to compare the results of different segmentation performances. Results: Optimal data composition was observed in the network trained for radiation dermatitis segmentation using skin photographs and skin-dose distribution. The average DSC, sensitivity, specificity and nMCC values of RGBLAB_D were 0.62, 0.61, 0.91 and 0.77, respectively, in faint radiation dermatitis, and 0.69, 0.78, 0.96 and 0.83, respectively, in severe radiation dermatitis. Conclusion: Our study showed that CNN-based radiation dermatitis segmentation in skin photographs of patients undergoing radiotherapy can describe radiation dermatitis severity and pattern. Our study could aid in objectifying the radiation dermatitis grading and analysing the reliable correlation between dosimetric factors and the morphology of radiation dermatitis. Highlights: The severity of radiation dermatitis can be segmented using skin photographs. Skin-dose distribution was important to segment the occurrence and severity of radiation dermatitis. Radiation dermatitis segmentation can objectify radiation dermatitis grade and evaluate the correlation with skin dose.