Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California.
Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Int J Radiat Oncol Biol Phys. 2019 Apr 1;103(5):1251-1260. doi: 10.1016/j.ijrobp.2018.11.048. Epub 2018 Nov 30.
Geometric indicators of contouring accuracy suffer from lack of clinical context in radiation therapy. To provide clinical relevance, treatment plans should be generated from the candidate contours, but manual planning could introduce confounding variations. Therefore, our objectives in this study were as follows: (1) determine the feasibility of using automated knowledge-based planning as an objective tool to generate dosimetric parameters for contour evaluation, (2) evaluate the correlation between geometric indices and dosimetric endpoints, and (3) report the dosimetric impact of multiple observations of head and neck target and organ-at-risk (OAR) volumes contoured by resident physicians.
Twenty-two resident physicians contoured the clinical target volumes, parotids, and cochleae for a nasopharyngeal cancer case, and expert-generated contours were defined as the gold standard for this study. A validated knowledge-based planning routine generated 67 treatment plans with various resident/gold-standard and target/OAR combinations. Dosimetric indices (dose to hottest 98% volume of planning target volume, and mean dose of OAR) were calculated on gold-standard contours. Commonly used geometric indices (Dice coefficients, Hausdorff maximum/mean/median distances, volume differences, and centroid distances) were also calculated. R quantified the correlation between geometric and dosimetric indices.
The correlation between geometric and dosimetric indices was weak (R < 0.2 for 61% of the correlations studied-77 of 126) and inconsistent (no single geometric index consistently exhibited superior/inferior correlation with dosimetric endpoints). The lack of consistent correlations between geometric and dosimetric indices resulted in the inability to define any geometric index thresholds for clinical acceptability. Geometric indices also exhibited a high propensity for false positives and false negatives as a classifier of dosimetric impact. Finally, we found substantial interresident contour variation, whether quantified using geometric or dosimetric indices, with significant negative dosimetric impact should these contours be used clinically.
Contour variation among resident physicians significantly affected dosimetric endpoints, highlighting the importance of resident education in head and neck anatomy delineation. Whenever available, dosimetric indices generated from automated planning should be used alongside geometric indices in radiation therapy contouring studies.
在放射治疗中,轮廓准确性的几何指标缺乏临床背景。为了提供临床相关性,应该根据候选轮廓生成治疗计划,但手动规划可能会引入混杂的变化。因此,我们在这项研究中的目标如下:(1)确定使用自动化基于知识的规划作为生成轮廓评估剂量参数的客观工具的可行性,(2)评估几何指标和剂量学终点之间的相关性,以及(3)报告由住院医师多次勾画头颈部靶区和器官危及器官(OAR)体积的剂量学影响。
22 名住院医师为鼻咽癌病例勾画了临床靶区、腮腺和耳蜗,专家生成的轮廓被定义为本研究的金标准。经过验证的基于知识的规划程序生成了 67 个具有各种住院医师/金标准和靶区/OAR 组合的治疗计划。在金标准轮廓上计算了剂量学指标(计划靶区体积 hottest 98% 体积的剂量和 OAR 的平均剂量)。还计算了常用的几何指标(Dice 系数、Hausdorff 最大/平均/中位数距离、体积差异和质心距离)。R 量化了几何和剂量学指标之间的相关性。
几何和剂量学指标之间的相关性较弱(在研究的 61%的相关性中,R<0.2-126 个相关性中有 77 个)且不一致(没有单个几何指标始终与剂量学终点表现出较好/较差的相关性)。几何和剂量学指标之间缺乏一致的相关性导致无法定义任何几何指标阈值以实现临床可接受性。几何指标作为剂量学影响的分类器也表现出很高的假阳性和假阴性倾向。最后,我们发现即使使用几何或剂量学指标进行量化,住院医师之间的轮廓变化也很大,如果这些轮廓在临床上使用,将会产生显著的负剂量学影响。
住院医师之间的轮廓变化显著影响剂量学终点,突出了头颈部解剖学勾画方面住院医师教育的重要性。在放射治疗轮廓研究中,只要有条件,就应该使用自动规划生成的剂量学指标与几何指标一起使用。