Abstract

Info

Title:: Application of automatic organs-at-risk segmentation based on artificial intelligence technology in thoracic tumors

Author(s):: WANG Peipei; LI Jinkai; LI Caihong; CHANG Zhigang; GU Xiaohuan; CAO Yuandong; Center of Radiation Oncology, Jiangsu Province Hospital, Nanjing 210029, China

Keywords:: thoracic tumor; artificial intelligence; organs-at-risk; automatic segmentation; radiotherapy

Abstract:: Objective To evaluate the geometric accuracy of automatic segmentation software based on artificial intelligence technology for segmenting the organs-at-risk (OAR) in patients with thoracic tumors, so as to provide a basis for its clinical application. Methods A total of 30 patients with thoracic tumors were enrolled in the study, and the thoracic OAR was automatically delineated by segmentation software and manually segmented by physicians. Three evaluation indexes, namely Hausdorff distance, Dice similarity coefficient and Jaccard coefficient, were used to evaluate the geometric consistency between automatic segmentation and manual segmentation. Results Among the Hausdorff distances of lung-L, lung-R, heart and spinal cord, the maximum Hausdorff distance was (22.31±4.50) mm in lung-R, and the minimum was (3.17±0.80) mm in spinal cord. The Dice similarity coefficient of all OAR (lung-L, lung-R, heart, spinal cord) was not less than 0.91. The mean value of Jaccard coefficient in lung-L and lung-R were greater than or equal to 0.95, while that in spinal cord and heart was 0.84±0.02 and 0.83±0.04, respectively. Conclusion The automatic segmentation software based on artificial intelligence technology can achieve a high accuracy and precision in thoracic OAR segmentation, which can meet the needs of clinical practices.