目的:在治疗计划中集成自动化流程的优点之一是减少了手动计划的可变性。这项研究旨在评估基于深度学习的自动计划解决方案是否也可以减少轮廓变化对早期乳腺癌治疗计划剂量的影响。
方法:使用自动和手动OAR对20名患者进行了自动和手动计划优化,包括两个肺,右乳房,心,和左前降支(LAD)动脉。在重新计算的剂量方面的差异(ΔDrcM,ΔDrcA)和重新优化的剂量(ΔDroM,ΔDroA)用于手动(M)和自动(A)计划,在手动结构上进行了评估。还探讨了几种几何相似性和剂量差异之间的相关性(Spearman检验)。
结果:发现右乳房和心脏的自动轮廓尺寸略小于手动轮廓,而LAD则大两倍以上。除心脏外,两种计划方法的重新计算的剂量差异均可忽略不计(ΔDrcM=-0.4Gy,ΔDrcA=-0.3Gy)和右乳房(ΔDrcM=-1.2Gy,ΔDrcA=-1.3Gy)最大剂量。重新优化的剂量差异被认为等同于肺和LAD的重新计算的剂量差异,虽然它们对于心脏来说明显较小(ΔDroM=-0.2Gy,ΔDroA=-0.2Gy)和右乳房(ΔDroM=-0.3Gy,ΔDroA=-0.9Gy)最大剂量。发现ΔDrcM有21个相关性,A(M=8,A=13)对于ΔDroM减少到4,A(M=3,A=1)。
结论:与手动计划相比,自动计划对轮廓变化的敏感性不相关,无论使用何种方法计算剂量差异。尽管如此,用于定义剂量差异的方法强烈影响相关性分析,导致剂量重新优化时高度降低,不管规划方法如何。
OBJECTIVE: One of the advantages of integrating automated processes in treatment planning is the reduction of manual planning variability. This study aims to assess whether a deep-learning-based auto-planning solution can also reduce the contouring variation-related impact on the planned dose for early-breast cancer treatment.
METHODS: Auto- and manual plans were optimized for 20 patients using both auto- and manual OARs, including both lungs, right breast, heart, and left-anterior-descending (LAD) artery. Differences in terms of recalculated dose (ΔDrcM,ΔDrcA) and reoptimized dose (ΔDroM,ΔDroA) for manual (M) and auto (A)-plans, were evaluated on manual structures. The correlation between several geometric similarities and dose differences was also explored (Spearman\'s test).
RESULTS: Auto-contours were found slightly smaller in size than manual contours for right breast and heart and more than twice larger for LAD. Recalculated dose differences were found negligible for both planning approaches except for heart (ΔDrcM=-0.4 Gy, ΔDrcA=-0.3 Gy) and right breast (ΔDrcM=-1.2 Gy, ΔDrcA=-1.3 Gy) maximum dose. Re-optimized dose differences were considered equivalent to recalculated ones for both lungs and LAD, while they were significantly smaller for heart (ΔDroM=-0.2 Gy, ΔDroA=-0.2 Gy) and right breast (ΔDroM =-0.3 Gy, ΔDroA=-0.9 Gy) maximum dose. Twenty-one correlations were found for ΔDrcM,A (M=8,A=13) that reduced to four for ΔDroM,A (M=3,A=1).
CONCLUSIONS: The sensitivity of auto-planning to contouring variation was found not relevant when compared to manual planning, regardless of the method used to calculate the dose differences. Nonetheless, the method employed to define the dose differences strongly affected the correlation analysis resulting highly reduced when dose was reoptimized, regardless of the planning approach.