Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Med Phys. 2019 Mar;46(3):1408-1425. doi: 10.1002/mp.13344. Epub 2019 Jan 21.
Proton dose distribution is sensitive to uncertainties in range estimation and patient positioning. Currently, the proton robustness is managed by worst-case scenario optimization methods, which are computationally inefficient. To overcome these challenges, we develop a novel intensity-modulated proton therapy (IMPT) optimization method that integrates dose fidelity with a sensitivity term that describes dose perturbation as the result of range and positioning uncertainties.
In the integrated optimization framework, the optimization cost function is formulated to include two terms: a dose fidelity term and a robustness term penalizing the inner product of the scanning spot sensitivity and intensity. The sensitivity of an IMPT scanning spot to perturbations is defined as the dose distribution variation induced by range and positioning errors. To evaluate the sensitivity, the spatial gradient of the dose distribution of a specific spot is first calculated. The spot sensitivity is then determined by the total absolute value of the directional gradients of all affected voxels. The fast iterative shrinkage-thresholding algorithm is used to solve the optimization problem. This method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The proposed sensitivity-regularized method (SenR) was implemented on both clinic target volume (CTV) and planning target volume (PTV). They were compared with conventional PTV-based optimization method (Conv) and CTV-based voxel-wise worst-case scenario optimization approach (WC).
Under the nominal condition without uncertainties, the three methods achieved similar CTV dose coverage, while the CTV-based SenR approach better spared organs at risks (OARs) compared with the WC approach, with an average reduction of [Dmean, Dmax] of [4.72, 3.38] GyRBE for the SBT cases and [2.54, 3.33] GyRBE for the H&N cases. The OAR sparing of the PTV-based SenR method was comparable with the WC method. The WC method, and SenR approaches all improved the plan robustness from the conventional PTV-based method. On average, under range uncertainties, the lowest [D95%, V95%, V100%] of CTV were increased from [93.75%, 88.47%, 47.37%] in the Conv method, to [99.28%, 99.51%, 86.64%] in the WC method, [97.71%, 97.85%, 81.65%] in the SenR-CTV method and [98.77%, 99.30%, 85.12%] in the SenR-PTV method, respectively. Under setup uncertainties, the average lowest [D95%, V95%, V100%] of CTV were increased from [95.35%, 94.92%, 65.12%] in the Conv method, to [99.43%, 99.63%, 87.12%] in the WC method, [96.97%, 97.13%, 77.86%] in the SenR-CTV method, and [98.21%, 98.34%, 83.88%] in the SenR-PTV method, respectively. The runtime of the SenR optimization is eight times shorter than that of the voxel-wise worst-case method.
We developed a novel computationally efficient robust optimization method for IMPT. The robustness is calculated as the spot sensitivity to both range and shift perturbations. The dose fidelity term is then regularized by the sensitivity term for the flexibility and trade-off between the dosimetry and the robustness. In the stress test, SenR is more resilient to unexpected uncertainties. These advantages in combination with its fast computation time make it a viable candidate for clinical IMPT planning.
质子剂量分布对范围估计和患者定位的不确定性敏感。目前，质子稳健性通过最坏情况场景优化方法进行管理，该方法计算效率低下。为了克服这些挑战，我们开发了一种新的强度调制质子治疗（IMPT）优化方法，该方法将剂量保真度与描述剂量扰动作为范围和定位不确定性结果的灵敏度项相结合。
在集成优化框架中，优化成本函数的公式包括两个项：剂量保真度项和惩罚范围和定位不确定性内积的稳健性项。IMPT 扫描点对扰动的灵敏度定义为剂量分布由于范围和定位误差引起的变化。为了评估灵敏度，首先计算特定点的剂量分布的空间梯度。然后，通过所有受影响体素的方向梯度的绝对值确定点灵敏度。使用快速迭代收缩阈值算法来解决优化问题。该方法在三个颅底肿瘤（SBT）患者和三个双侧头颈部（H&N）患者上进行了测试。提出的基于灵敏度的正则化方法（SenR）分别在临床靶区（CTV）和计划靶区（PTV）上实施。将它们与传统的 PTV 为基础的优化方法（Conv）和基于体素的最坏情况场景优化方法（WC）进行了比较。
在没有不确定性的名义条件下，三种方法在 CTV 剂量覆盖方面表现相似，而基于 CTV 的 SenR 方法与 WC 方法相比，更好地保护了危及器官（OAR），平均减少了[Dmean，Dmax]分别为 [4.72，3.38]GyRBE 的 SBT 病例和 [2.54，3.33]GyRBE 的 H&N 病例。基于 PTV 的 SenR 方法的 OAR 保护与 WC 方法相当。WC 方法和 SenR 方法均提高了常规 PTV 为基础的方法的稳健性。平均而言，在范围不确定性下，CTV 的最低[D95%，V95%，V100%]分别从 Conv 方法的[93.75%，88.47%，47.37%]增加到 WC 方法的[99.28%，99.51%，86.64%]，SenR-CTV 方法的[97.71%，97.85%，81.65%]和 SenR-PTV 方法的[98.77%，99.30%，85.12%]。在定位不确定性下，CTV 的平均最低[D95%，V95%，V100%]分别从 Conv 方法的[95.35%，94.92%，65.12%]增加到 WC 方法的[99.43%，99.63%，87.12%]，SenR-CTV 方法的[96.97%，97.13%，77.86%]和 SenR-PTV 方法的[98.21%，98.34%，83.88%]。SenR 优化的运行时间比基于体素的最坏情况方法短八倍。
我们开发了一种新的计算效率高的 IMPT 稳健性优化方法。稳健性是通过范围和移位扰动的点灵敏度计算的。然后，通过灵敏度项对剂量保真度项进行正则化，以在剂量学和稳健性之间具有灵活性和折衷。在压力测试中，SenR 对意外不确定性更具弹性。这些优势加上其快速的计算时间使其成为临床 IMPT 计划的可行候选方案。
