Johnson Casey L, Press Robert H, Simone Charles B, Shen Brian, Tsai Pingfang, Hu Lei, Yu Francis, Apinorasethkul Chavanon, Ackerman Christopher, Zhai Huifang, Lin Haibo, Huang Sheng
New York Proton Center, New York, NY, United States.
National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, China.
Front Oncol. 2024 Jul 11;14:1375096. doi: 10.3389/fonc.2024.1375096. eCollection 2024.
To evaluate organ at risk (OAR) auto-segmentation in the head and neck region of computed tomography images using two different commercially available deep-learning-based auto-segmentation (DLAS) tools in a single institutional clinical applications.
Twenty-two OARs were manually contoured by clinicians according to published guidelines on planning computed tomography (pCT) images for 40 clinical head and neck cancer (HNC) cases. Automatic contours were generated for each patient using two deep-learning-based auto-segmentation models-Manteia AccuContour and MIM ProtégéAI. The accuracy and integrity of autocontours (ACs) were then compared to expert contours (ECs) using the Sørensen-Dice similarity coefficient (DSC) and Mean Distance (MD) metrics.
ACs were generated for 22 OARs using AccuContour and 17 OARs using ProtégéAI with average contour generation time of 1 min/patient and 5 min/patient respectively. EC and AC agreement was highest for the mandible (DSC 0.90 ± 0.16) and (DSC 0.91 ± 0.03), and lowest for the chiasm (DSC 0.28 ± 0.14) and (DSC 0.30 ± 0.14) for AccuContour and ProtégéAI respectively. Using AccuContour, the average MD was<1mm for 10 of the 22 OARs contoured, 1-2mm for 6 OARs, and 2-3mm for 6 OARs. For ProtégéAI, the average mean distance was<1mm for 8 out of 17 OARs, 1-2mm for 6 OARs, and 2-3mm for 3 OARs.
Both DLAS programs were proven to be valuable tools to significantly reduce the time required to generate large amounts of OAR contours in the head and neck region, even though manual editing of ACs is likely needed prior to implementation into treatment planning. The DSCs and MDs achieved were similar to those reported in other studies that evaluated various other DLAS solutions. Still, small volume structures with nonideal contrast in CT images, such as nerves, are very challenging and will require additional solutions to achieve sufficient results.
在单一机构的临床应用中，使用两种不同的基于深度学习的商用自动分割（DLAS）工具，评估计算机断层扫描图像头颈部区域的危及器官（OAR）自动分割。
根据已发表的头颈部癌（HNC）临床病例计划计算机断层扫描（pCT）图像指南，临床医生手动勾勒出22个OAR的轮廓。使用两种基于深度学习的自动分割模型——Manteia AccuContour和MIM ProtégéAI为每位患者生成自动轮廓。然后使用索伦森-戴斯相似系数（DSC）和平均距离（MD）指标，将自动轮廓（AC）的准确性和完整性与专家轮廓（EC）进行比较。
使用AccuContour为22个OAR生成了AC，使用ProtégéAI为17个OAR生成了AC，平均轮廓生成时间分别为1分钟/患者和5分钟/患者。下颌骨的EC和AC一致性最高（AccuContour的DSC为0.90±0.16，ProtégéAI的DSC为0.91±0.03），视交叉的一致性最低（AccuContour的DSC为0.28±0.14，ProtégéAI的DSC为0.30±0.14）。使用AccuContour，在勾勒出的22个OAR中，10个OAR的平均MD<1mm，6个OAR的平均MD为1 - 2mm，6个OAR的平均MD为2 - 3mm。对于ProtégéAI，17个OAR中有8个的平均平均距离<1mm，6个OAR的平均平均距离为1 - 2mm，3个OAR的平均平均距离为2 - 3mm。
尽管在将AC应用于治疗计划之前可能需要进行手动编辑，但这两种DLAS程序都被证明是有价值的工具，可显著减少在头颈部区域生成大量OAR轮廓所需的时间。所获得的DSC和MD与其他评估各种其他DLAS解决方案的研究报告结果相似。尽管如此，CT图像中对比度不理想的小体积结构，如神经，仍然极具挑战性，需要额外的解决方案才能获得足够的结果。
