Baroudi Hana, Che Fru Leonard, Schofield Deborah, Roniger Dominique L, Nguyen Callistus, Hancock Donald, Chung Christine, Beadle Beth M, Gifford Kent A, Netherton Tucker, Niedzielski Joshua S, Melancon Adam, Muruganandham Manickam, Khan Meena, Shaitelman Simona F, Shete Sanjay, Murina Patricia, Venencia Daniel, Thengumpallil Sheeba, Vrieling Conny, Zhang Joy, Mitchell Melissa P, Court Laurence E
The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas, USA.
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Med Phys. 2025 Mar;52(3):1779-1788. doi: 10.1002/mp.17588. Epub 2024 Dec 19.
Automation in radiotherapy presents a promising solution to the increasing cancer burden and workforce shortages. However, existing automated methods for breast radiotherapy lack a comprehensive, end-to-end solution that meets varying standards of care.
This study aims to develop a complete portfolio of automated radiotherapy treatment planning for intact breasts, tailored to individual patient factors, clinical approaches, and available resources.
We developed five automated conventional treatment approaches and utilized an established RapidPlan model for volumetric arc therapy. These approaches include conventional tangents for whole breast treatment, two variants for supraclavicular nodes (SCLV) treatment with/without axillary nodes, and two options for comprehensive regional lymph nodes treatment. The latter consists of wide tangents photon fields with a SCLV field, and a photon tangents field with a matched electron field to treat the internal mammary nodes (IMNs), and a SCLV field. Each approach offers the choice of a single or two isocenter setup (with couch rotation) to accommodate a wide range of patient sizes. All algorithms start by automatically generating contours for breast clinical target volume, regional lymph nodes, and organs at risk using an in-house nnU-net deep learning models. Gantry angles and field shapes are then automatically generated and optimized to ensure target coverage while limiting the dose to nearby organs. The dose is optimized using field weighting for the lymph nodes fields and an automated field-in-field approach for the tangents. These algorithms were integrated into the RayStation treatment planning system and tested for clinical acceptability on 15 internal whole breast patients (150 plans) and 40 external patients from four different institutions in Switzerland, Argentina, Iran, and the USA (360 plans). Evaluation criteria included ensuring adequate coverage of targets and adherence to dose constraints for normal structures. A breast radiation oncologist reviewed the single institution dataset for clinical acceptability (5-point scale) and a physicist evaluated the multi-institutional dataset (use as is or edit).
The dosimetric evaluation across all datasets (510 plans) showed that 100% of the automated plans met the dose coverage requirements for the breast, 99% for the SCLV, 98% for the axillary nodes, and 91% for the IMN. As expected, hot spots were more prevalent when multiple fields were combined. For the heart, ipsilateral lung, and contralateral breast, automated plans met constraints for 95%, 92%, and 95% of the plans, respectively. Physician evaluation of the 15 internal patients indicated that all automated plans were clinically acceptable with minor edits. Notably, the use of automated contours with the RapidPlan model resulted in plans that were immediately ready for use in 73% of cases (95% confidence interval, 95% CI [51- 96]) of patients, with the remaining cases requiring minor stylistic edits. Similarly, the physicist's review of the 40 multi-institution patients showed that the auto-plans were ready for use 79% (95% CI [73,85]) of the time (95% CI [73,85]), with edits needed for the remaining cases.
This study demonstrates the feasibility of a comprehensive automated treatment planning model for whole breast radiotherapy, effectively accommodating diverse treatment paradigms.
放射治疗自动化为日益增加的癌症负担和劳动力短缺问题提供了一个有前景的解决方案。然而，现有的乳腺放射治疗自动化方法缺乏一个全面的、端到端的解决方案来满足不同的护理标准。
本研究旨在开发一套完整的针对完整乳房的自动化放射治疗计划组合，根据个体患者因素、临床方法和可用资源进行定制。
我们开发了五种自动化传统治疗方法，并利用一个已建立的用于容积弧形治疗的RapidPlan模型。这些方法包括用于全乳治疗的传统切线野，用于锁骨上淋巴结（SCLV）治疗的两种变体（有/无腋窝淋巴结），以及用于全面区域淋巴结治疗的两种选择。后者包括带有SCLV野的宽切线光子野，以及带有匹配电子野以治疗内乳淋巴结（IMN）和SCLV野的光子切线野。每种方法都提供了单等中心或双等中心设置（带治疗床旋转）的选择，以适应各种体型的患者。所有算法首先使用内部的nnU-net深度学习模型自动生成乳腺临床靶区、区域淋巴结和危及器官的轮廓。然后自动生成并优化机架角度和射野形状，以确保靶区覆盖，同时限制对附近器官的剂量。使用淋巴结野的射野权重和切线野的自动子野技术来优化剂量。这些算法被集成到RayStation治疗计划系统中，并在来自瑞士、阿根廷、伊朗和美国四个不同机构的15例内部全乳患者（150个计划）和40例外部患者（360个计划）上进行了临床可接受性测试。评估标准包括确保靶区有足够的覆盖以及符合正常结构的剂量限制。一位乳腺放射肿瘤学家审查了单机构数据集的临床可接受性（5分制），一位物理学家评估了多机构数据集（直接使用或编辑）。
对所有数据集（510个计划）的剂量学评估表明，100%的自动化计划满足乳腺的剂量覆盖要求，锁骨上淋巴结为99%，腋窝淋巴结为98%，内乳淋巴结为91%。正如预期的那样，当多个射野组合时热点更普遍。对于心脏、同侧肺和对侧乳腺，自动化计划分别在95%、92%和95%的计划中满足限制。对15例内部患者的医生评估表明，所有自动化计划在进行少量编辑后临床上均可接受。值得注意的是，使用带有RapidPlan模型的自动轮廓生成，在73%（95%置信区间，95%CI[51 - 96]）的患者病例中生成的计划可立即使用，其余病例需要进行少量风格编辑。同样，物理学家对40例多机构患者的审查表明，自动计划在79%（95%CI[73,85]）的时间内可立即使用（95%CI[73,85]），其余病例需要编辑。
本研究证明了全乳放射治疗综合自动化治疗计划模型的可行性，有效地适应了不同的治疗模式。
