Purpose: Radiation dermatitis is one of the most common adverse events in patients undergoing radiotherapy. However, the objective evaluation of this condition is difficult to provide because the clinical evaluation of radiation dermatitis is made by visual assessment based on Common Terminology Criteria for Adverse Events (CTCAE). Therefore, we created a radiation dermatitis grading support system (RDGS) using a deep convolutional neural network (DCNN) and then evaluated the effectiveness of the RDGS.
Methods: The DCNN was trained with a dataset that comprised 647 clinical skin images graded with radiation dermatitis (Grades 1-4) at our center from April 2011 to May 2019. We created the datasets by mixing data augmentation images generated by image conversion and images generated by Poisson image editing using the hybrid generation method (Hyb) against lowvolume severe dermatitis (Grade 4). We then evaluated the classification accuracy of RDGS based on the hybrid generation method (Hyb-RDGS).
Results: The overall accuracy of the Hyb-RDGS was 85.1%, which was higher than that of the data augmentation method generally used for image generation.
Conclusion: Effectiveness of the Hyb-RDGS using Poisson image editing was suggested. This result shows a possible supporting system for objective evaluation in grading radiation dermatitis.
Keywords: Poisson image editing; deep learning; radiation dermatitis; radiation dermatitis grading support system; radiotherapy.