PDF(Pubmed)
Abstract：
UNASSIGNED: While deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario.
UNASSIGNED: We collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny.
UNASSIGNED: Dose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%).
UNASSIGNED: This study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario.
UNASSIGNED: We collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny.
UNASSIGNED: Dose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%).
UNASSIGNED: This study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario.
摘要：
■虽然深度学习已显示出自动化放射治疗计划的前景，它在使用线性加速器上的固定场强度调制放射治疗（IMRT）治疗脑转移瘤的立体定向放射外科（SRS）的特定场景中的应用仍然有限。这项工作旨在开发和验证为该场景量身定制的深度学习指导的自动计划协议。
■我们收集了70个针对孤立性脑转移的SRS计划，其中36例用于培训，34例用于测试。测试案例来自两个不同的临床机构。所设想的自动计划过程包括(1):由深度学习算法促进的临床剂量预测(2);经由以体素为中心的剂量模拟将预测剂量转换为可执行计划(3);采用精确剂量计结合线性加速器来验证所设想的计划。剂量预测范例是通过工程和完善两个三维UNet架构(UNet和AttUNet)建立的。输入参数包括来自临床计划的计算机断层扫描扫描以及在潜在风险器官（OAR）旁边的焦点划分；随后的输出表现为针对每个病例量身定制的3D剂量矩阵。
■两种模型得出的剂量估算均反映了手动计划，并遵守了临床规定。正如双重模型所预测的那样，OAR的最高剂量和平均剂量与手动计划中的剂量没有明显偏离(P值≥0.05).与基础UNet相比，AttUNet显示出可喜的结果。预测剂量显示出明显的剂量梯度，峰值浓度位于目标附近。可执行计划符合临床剂量测定基准，并与其相关的验证评估保持一致（3mm/3％的100％伽马批准率）。
■这项研究展示了一种用于脑转移瘤的基于固定场IMRT的SRS的自动化计划技术。设想的计划符合临床要求，可以跨中心复制，在交付中可以实现。这代表了针对该特定场景的自动化范例的进展。
■我们收集了70个针对孤立性脑转移的SRS计划，其中36例用于培训，34例用于测试。测试案例来自两个不同的临床机构。所设想的自动计划过程包括(1):由深度学习算法促进的临床剂量预测(2);经由以体素为中心的剂量模拟将预测剂量转换为可执行计划(3);采用精确剂量计结合线性加速器来验证所设想的计划。剂量预测范例是通过工程和完善两个三维UNet架构(UNet和AttUNet)建立的。输入参数包括来自临床计划的计算机断层扫描扫描以及在潜在风险器官（OAR）旁边的焦点划分；随后的输出表现为针对每个病例量身定制的3D剂量矩阵。
■两种模型得出的剂量估算均反映了手动计划，并遵守了临床规定。正如双重模型所预测的那样，OAR的最高剂量和平均剂量与手动计划中的剂量没有明显偏离(P值≥0.05).与基础UNet相比，AttUNet显示出可喜的结果。预测剂量显示出明显的剂量梯度，峰值浓度位于目标附近。可执行计划符合临床剂量测定基准，并与其相关的验证评估保持一致（3mm/3％的100％伽马批准率）。
■这项研究展示了一种用于脑转移瘤的基于固定场IMRT的SRS的自动化计划技术。设想的计划符合临床要求，可以跨中心复制，在交付中可以实现。这代表了针对该特定场景的自动化范例的进展。
