Farrugia Mark, Yu Han, Singh Anurag K, Malhotra Harish
Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, United States.
Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, United States.
World J Clin Oncol. 2021 Feb 24;12(2):95-102. doi: 10.5306/wjco.v12.i2.95.
Radiation dose to specific cardiac substructures can have a significant on treatment related morbidity and mortality, yet definition of these structures is labor intensive and not standard. Autosegmentation software may potentially address these issues, however it is unclear whether this approach can be broadly applied across different treatment planning conditions. We investigated the feasibility of autosegmentation of the cardiac substructures in four-dimensional (4D) computed tomography (CT), respiratory-gated, non-contrasted imaging.
To determine whether autosegmentation can be successfully employed on 4DCT respiratory-gated, non-contrasted imaging.
We included patients who underwent stereotactic body radiation therapy for inoperable, early-stage non-small cell lung cancer from 2007 to 2019. All patients were simulated 4DCT imaging with respiratory gating without intravenous contrast. Generated structure quality was evaluated by degree of required manual edits and volume discrepancy between the autocontoured structures and its edited sister structure.
Initial 17-structure cardiac atlas was generated with 20 patients followed by three successive iterations of 10 patients using MIM software. The great vessels and heart chambers were reliably autosegmented with most edits considered minor. In contrast, coronary arteries either failed to be autosegmented or the generated structures required major alterations necessitating deletion and manual definition. Similarly, the generated mitral and tricuspid valves were poor whereas the aortic and pulmonary valves required at least minor and moderate changes respectively. For the majority of subsites, the additional samples did not appear to substantially impact the quality of generated structures. Volumetric analysis between autosegmented and its manually edited sister structure yielded comparable findings to the physician-based assessment of structure quality.
The use of MIM software with 30-sample subject library was found to be useful in delineating many of the heart substructures with acceptable clinical accuracy on respiratory-gated 4DCT imaging. Small volume structures, such as the coronary arteries were poorly autosegmented and require manual definition.
特定心脏亚结构的辐射剂量可能对治疗相关的发病率和死亡率有重大影响，然而这些结构的定义需要耗费大量人力且不标准。自动分割软件可能潜在地解决这些问题，但是尚不清楚这种方法是否能广泛应用于不同的治疗计划条件。我们研究了在四维（4D）计算机断层扫描（CT）、呼吸门控、非增强成像中自动分割心脏亚结构的可行性。
确定自动分割是否能成功应用于4DCT呼吸门控、非增强成像。
我们纳入了2007年至2019年因无法手术的早期非小细胞肺癌接受立体定向体部放射治疗的患者。所有患者均接受了呼吸门控且无静脉造影剂的4DCT模拟成像。通过所需手动编辑的程度以及自动勾勒结构与其编辑后的姊妹结构之间的体积差异来评估生成结构的质量。
最初用20名患者生成了包含17个结构的心脏图谱，随后使用MIM软件对10名患者进行了连续三次迭代。大血管和心腔被可靠地自动分割，大多数编辑被认为是小改动。相比之下，冠状动脉要么未能被自动分割，要么生成的结构需要进行重大改动，需要删除并手动定义。同样，生成的二尖瓣和三尖瓣较差，而主动脉瓣和肺动脉瓣分别至少需要进行小改动和中等改动。对于大多数亚部位，额外的样本似乎并未对生成结构的质量产生实质性影响。自动分割结构与其手动编辑的姊妹结构之间的体积分析得出的结果与基于医生的结构质量评估结果相当。
发现在呼吸门控4DCT成像上，使用具有30个样本主体库的MIM软件有助于以可接受的临床准确性勾勒许多心脏亚结构轮廓。小体积结构，如冠状动脉，自动分割效果较差，需要手动定义。
