Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries

作者：Solomon Kibudde, Awusi Kavuma, Yao Hao, Tianyu Zhao, Hiram A. Gay, Jacaranda van Rheenen, Pavan Jhaveri, Minjmaa Minjgee, Enkhsetseg Vanchinbazar, Urdenekhuu Nansalmaa, Baozhou Sun · 发表于：Advances in Radiation Oncology · 年份：2024 · DOI：10.1016/j.adro.2024.101638 · 被引用次数：7 · 研究领域：Advanced Radiotherapy Techniques、Advances in Oncology and Radiotherapy、Brain Metastases and Treatment

Purpose: Radiation therapy (RT) processes require significant human resources and expertise, creating a barrier to rapid RT deployment in low- and middle-income countries (LMICs). Accurate segmentation of tumor targets and organs at risk (OARs) is crucial for optimal RT. This study assessed the impact of artificial intelligence (AI)-based autosegmentation of OARs in 2 LMICs. Methods and Materials: Ten patients, comprising 5 head and neck (HN) cancer patients and 5 prostate cancer patients, were randomly selected. Planning computed tomography images were subjected to autosegmentation using an Food and Drug Administration-approved AI software tool and manual segmentation by experienced radiation oncologists from 2 LMIC RT clinics. The control data, obtained from a large academic institution in the United States, consisted of contours obtained by an experienced radiation oncologist. The segmentation time, DICE similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated. Results: AI significantly reduced segmentation time, averaging 2 minutes per patient, compared with 57 to 84 minutes for manual contouring in LMICs. Compared with the control data, the AI pelvic contours provided better agreement than did the LMIC manual contours (mean DSC of 0.834 vs 0.807 in LMIC1 and 0.844 vs 0.801 in LMIC2). For HN contours, AI provided better agreement for the majority of OAR contours than manual contours in LMIC1 (mean DSC: 0.823 vs 0.821) or LMIC2 (mean DSC: ...