Kibudde Solomon, Kavuma Awusi, Hao Yao, Zhao Tianyu, Gay Hiram, Van Rheenen Jacaranda, Jhaveri Pavan Mukesh, Minjgee Minjmaa, Vanchinbazar Enkhsetseg, Nansalmaa Urdenekhuu, Sun Baozhou
Division of Radiation Oncology, Uganda Cancer Institute, Kampala, Uganda.
Division of Radiation Oncology, Washington University in St. Louis, St. Louis, Missouri.
Adv Radiat Oncol. 2024 Oct 5;9(11):101638. doi: 10.1016/j.adro.2024.101638. eCollection 2024 Nov.
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.
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.
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: 0.792 vs 0.748). Neither the AI nor LMIC manual contours had good agreement with the control data (DSC < 0.600) for the optic nerves, chiasm, and cochlea.
AI-based autosegmentation generates OAR contours of comparable quality to manual segmentation for both pelvic and HN cancer patients in LMICs, with substantial time savings.
放射治疗（RT）流程需要大量人力资源和专业知识，这成为低收入和中等收入国家（LMICs）快速部署RT的障碍。准确分割肿瘤靶区和危及器官（OARs）对于优化RT至关重要。本研究评估了基于人工智能（AI）的OARs自动分割在两个LMICs中的影响。
随机选择10例患者，包括5例头颈（HN）癌患者和5例前列腺癌患者。计划计算机断层扫描图像使用美国食品药品监督管理局批准的AI软件工具进行自动分割，并由来自两个LMIC RT诊所的经验丰富的放射肿瘤学家进行手动分割。从美国一家大型学术机构获得的对照数据由经验丰富的放射肿瘤学家获得的轮廓组成。评估分割时间、DICE相似系数（DSC）、豪斯多夫距离和平均表面距离。
与LMICs中手动勾勒轮廓的57至84分钟相比，AI显著缩短了分割时间，平均每位患者2分钟。与对照数据相比，AI骨盆轮廓比LMICs手动轮廓具有更好的一致性（LMIC1中平均DSC为0.834对0.807，LMIC2中为0.844对0.801）。对于HN轮廓，在LMIC1（平均DSC：0.823对0.821）或LMIC2（平均DSC：0.792对0.748）中，AI对大多数OAR轮廓提供了比手动轮廓更好的一致性。对于视神经、视交叉和耳蜗，AI和LMIC手动轮廓与对照数据均无良好一致性（DSC<0.600）。
基于AI的自动分割为LMICs中的骨盆和HN癌患者生成了与手动分割质量相当的OAR轮廓，同时大幅节省了时间。
