Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America.
Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Phys Med Biol. 2024 Sep 20;69(19). doi: 10.1088/1361-6560/ad780b.
. Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice when the number is sufficiently large (e.g., >100). Our proposed deep learning (DL)-based method addressed this issue by avoiding the intermediate dose calculation step and instead directly predicting the percentile dose distribution from the nominal dose distribution using a DL model. In this study, we sought to validate this DL-based statistical robustness evaluation method for efficient and accurate robustness quantification in head and neck (H&N) intensity-modulated proton therapy with diverse beam configurations and multifield optimization.. A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile dose distributions of uncertainty scenarios using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site.. The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets.. Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites.
先前的稳健性评估方法依赖于对许多不确定性场景进行剂量计算，当数量较少（例如，约 8 个）时，这种方法无法提供统计学意义，或者当数量足够大（例如，>100 个）时，在日常临床实践中变得不可行。我们提出的基于深度学习（DL）的方法通过避免中间剂量计算步骤，而是使用 DL 模型直接从名义剂量分布预测百分位数剂量分布，解决了这个问题。在这项研究中，我们旨在验证这种基于 DL 的统计稳健性评估方法，以便在具有不同束配置和多场优化的头颈部（H＆N）强度调制质子治疗中进行高效和准确的稳健性量化。使用名义剂量和计划 CT 图像，训练了一个密集的、扩张的 3D U-net，以预测不确定性场景的第 5 和 95 百分位数剂量分布。数据集包括 582 例 H＆N 癌症患者的质子治疗计划。通过对每个患者进行 600 次剂量重新计算，代表随机采样的不确定性场景，为每位患者估计了百分位数值。对于具有和不具有束掩模以及不同束配置的 H＆N 癌症患者，包括不同的束角度、治疗床角度和束数量，进行了不同模型的综合比较。还评估了基于 H＆N 和前列腺癌患者混合数据集训练的模型与基于特定于任一部位癌症患者数据训练的模型的性能。基于 2mm2％的平均伽玛指数，第 5 和 95 百分位数剂量体积的一致性均超过 97％。剂量-体积直方图误差分析表明，来自组合训练集的预测结果产生的平均误差和标准偏差通常与特定患者训练数据集的结果相似。我们提出的用于质子治疗计划稳健性评估的基于 DL 的方法可以针对给定置信水平快速准确地预测百分位数剂量，而与束排列和癌症部位无关。这种多功能性使我们的模型成为评估跨各种癌症部位质子治疗稳健性的有价值工具。
