Borges Murilo Guimarães, Gruenwaldt Joyce, Barsanelli Danilo Matheus, Ishikawa Karina Emy, Stuart Silvia Radwanski
Department of Medical Physics, Centre for Biomedical Engineering (CEB), University of Campinas, Rua Alexander Fleming, 163, Cidade Universitária, 13083-881 Campinas, SP, Brazil; Hospital da Mulher Prof. Dr. José Aristodemo Pinotti (CAISM), University of Campinas, R. Alexander Fleming, 101, Cidade Universitária, 13083-881 Campinas, SP, Brazil.
Centro de Oncologia Campinas (COC), R. Alberto de Salvo, 311, Barão Geraldo, 13084-759 Campinas, SP, Brazil; Department of Radiotherapy, Hospital das Clínicas, University of Campinas, R. Vital Brasil, 251, Cidade Universitária, 13083-888 Campinas, SP, Brazil.
J Med Imaging Radiat Sci. 2025 Mar;56(2):101844. doi: 10.1016/j.jmir.2024.101844. Epub 2024 Dec 30.
Radiotherapy is a crucial part of breast cancer treatment. Precision in dose assessment is essential to minimize side effects. Traditionally, anatomical structures are delineated manually, a time-consuming process subject to variability. automatic segmentation, including methods based on multiple atlases and deep learning, offers a promising alternative. For the radiotherapy treatment of the left breast, the RTOG 1005 protocol highlights the importance of cardiac delineation and the need to minimize cardiac exposure to radiation. Our study aims to evaluate dose distribution in auto-segmented substructures and establish models to correlate them with dose in the cardiac area.
Anatomical structures were auto-segmented using TotalSegmentator and Limbus AI. The relationship between the volume of the cardiac area and of organs at risk was assessed using log-linear regressions.
The mean dose distribution was considerable for LAD (left anterior descending coronary artery), heart, and left ventricle. The volumetric distribution of organs at risk is evaluated for specific RTOG 1005 isodoses. We highlight the greater variability in the absolute volumetric evaluation. Log-linear regression models are presented to estimate dose constraint parameters. We highlight a greater number of highly correlated comparisons for absolute dose-volume assessment.
Dose-volume assessment protocols in patients with left breast cancer often neglect cardiac substructures. However, automatic tools can overcome these technical difficulties. In this study, we correlated the dose in the cardiac area with the doses in specific substructures and suggested limits for planning evaluation. Our data also indicates that statistical models could be applied in the assessment of those substructures where an automatic segmentation tool is not available. Our data also shows a benefit in reporting absolute dose-volume thresholds for future cause-effect assessments.
放射治疗是乳腺癌治疗的关键部分。精确的剂量评估对于将副作用降至最低至关重要。传统上，解剖结构是手动勾勒的，这是一个耗时且易变的过程。自动分割，包括基于多图谱和深度学习的方法，提供了一种有前景的替代方案。对于左乳的放射治疗，RTOG 1005方案强调了心脏勾勒的重要性以及将心脏受辐射暴露降至最低的必要性。我们的研究旨在评估自动分割的子结构中的剂量分布，并建立模型将它们与心脏区域的剂量相关联。
使用TotalSegmentator和Limbus AI对解剖结构进行自动分割。使用对数线性回归评估心脏区域体积与危及器官体积之间的关系。
左前降支冠状动脉、心脏和左心室的平均剂量分布相当可观。针对特定的RTOG 1005等剂量线评估危及器官的体积分布。我们强调绝对体积评估中存在更大的变异性。提出了对数线性回归模型以估计剂量约束参数。我们强调在绝对剂量-体积评估中有更多高度相关的比较。
左乳腺癌患者的剂量-体积评估方案通常忽略心脏子结构。然而，自动工具可以克服这些技术困难。在本研究中，我们将心脏区域的剂量与特定子结构中的剂量相关联，并提出了计划评估的限值。我们的数据还表明，统计模型可应用于无法使用自动分割工具的那些子结构的评估。我们的数据还显示了报告绝对剂量-体积阈值对于未来因果评估的益处。
