Tomita Fumihiro, Yamauchi Ryohei, Akiyama Shinobu, Hirano Miki, Masuda Tomoyuki, Ishikura Satoshi
Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, JPN.
Cureus. 2025 Mar 27;17(3):e81307. doi: 10.7759/cureus.81307. eCollection 2025 Mar.
This study aimed to verify whether a commercial deep learning-based automatic segmentation (DLS) method can maintain contour geometric accuracy post-update and to propose a streamlined validation method that minimizes the burden on clinical workflows. This study included 109 participants. Radiation oncologists used computed tomography (CT) imaging to identify 28 organs located in the head and neck, chest, abdomen, and pelvic regions. Contours were delineated on CT images using AI-Rad Companion Organs RT (AIRC; Siemens Healthineers, Erlangen, Germany) versions VA30, VA50, and VA50. The Dice similarity coefficient, maximum Hausdorff distance, and mean distance to agreement were calculated to identify contours with significant differences among versions. To evaluate the identified contours, the ground truth was defined as the contour delineated by radiation oncologists, and the geometric indices for VA30, VA50, and VA60 were recalculated. Statistical analysis was performed on the geometric indices to verify differences between each version. Among the 28 contours evaluated, nine organs did not satisfy the established criteria. Statistical analysis revealed that the brain, rectum, and bladder contours differed substantially across AIRC versions. In particular, the pre-update rectum contour had a mean (range) Hausdorff distance of 0.76 (0.40-1.16), whereas the post-update rectum contour exhibited lower quality, with a Hausdorff distance of 1.13 (0.24-5.68). Therefore, commercial DLS methods that undergo regular updates must be reassessed for quality in each region of interest. The proposed method can help reduce the burden on clinical workflows while appropriately evaluating post-update DLS performance.
本研究旨在验证一种基于深度学习的商业自动分割（DLS）方法在更新后能否保持轮廓几何精度，并提出一种简化的验证方法，以尽量减少对临床工作流程的负担。本研究纳入了109名参与者。放射肿瘤学家使用计算机断层扫描（CT）成像来识别位于头颈部、胸部、腹部和盆腔区域的28个器官。使用AI-Rad Companion Organs RT（AIRC；西门子医疗，德国埃尔兰根）VA30、VA50和VA60版本在CT图像上勾勒轮廓。计算骰子相似系数、最大豪斯多夫距离和平均一致距离，以识别不同版本之间存在显著差异的轮廓。为了评估识别出的轮廓，将金标准定义为放射肿瘤学家勾勒的轮廓，并重新计算VA30、VA50和VA60的几何指标。对几何指标进行统计分析，以验证每个版本之间的差异。在评估的28个轮廓中，9个器官不符合既定标准。统计分析表明，大脑、直肠和膀胱轮廓在AIRC不同版本之间存在显著差异。特别是，更新前直肠轮廓的平均（范围）豪斯多夫距离为0.76（0.40-1.16），而更新后直肠轮廓质量较低，豪斯多夫距离为1.13（0.24-5.68）。因此，对于定期更新的商业DLS方法，必须在每个感兴趣区域重新评估其质量。所提出的方法有助于减少临床工作流程的负担，同时适当地评估更新后DLS的性能。
