Stanford University, Department of Radiation Oncology, Stanford, CA 94305, United States of America.
The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, United States of America.
Phys Med Biol. 2022 Jul 27;67(15). doi: 10.1088/1361-6560/ac7fd6.
Small field dosimetry is significantly different from the dosimetry of broad beams due to loss of electron side scatter equilibrium, source occlusion, and effects related to the choice of detector. However, use of small fields is increasing with the increase in indications for intensity-modulated radiation therapy and stereotactic body radiation therapy, and thus the need for accurate dosimetry is ever more important. Here we propose to leverage machine learning (ML) strategies to reduce the uncertainties and increase the accuracy in determining small field output factors (OFs). Linac OFs from a Varian TrueBeam STx were calculated either by the treatment planning system (TPS) or measured with a W1 scintillator detector at various multi-leaf collimator (MLC) positions, jaw positions, and with and without contribution from leaf-end transmission. The fields were defined by the MLCs with the jaws at various positions. Field sizes between 5 and 100 mm were evaluated. Separate ML regression models were generated based on the TPS calculated or the measured datasets. Accurate predictions of small field OFs at different field sizes (FSs) were achieved independent of jaw and MLC position. A mean and maximum % relative error of 0.38 ± 0.39% and 3.62%, respectively, for the best-performing models based on the measured datasets were found. The prediction accuracy was independent of contribution from leaf-end transmission. Several ML models for predicting small field OFs were generated, validated, and tested. Incorporating these models into the dose calculation workflow could greatly increase the accuracy and robustness of dose calculations for any radiotherapy delivery technique that relies heavily on small fields.
小射野剂量学与宽束剂量学有显著差异，这是由于电子侧向散射平衡的丧失、源遮挡以及与探测器选择相关的效应所致。然而，随着强度调制放射治疗和立体定向体部放射治疗适应证的增加，小射野的应用越来越广泛，因此对准确剂量学的需求变得更加重要。在这里，我们提出利用机器学习（ML）策略来降低不确定性并提高确定小射野输出因子（OFs）的准确性。在各种多叶准直器（MLC）位置、机架位置以及是否有叶片末端透射的情况下，通过瓦里安 TrueBeam STx 的治疗计划系统（TPS）计算或使用 W1 闪烁体探测器测量，计算了 Linac 的 OFs。通过 MLC 定义射野，机架位置为各种位置。评估了 5 至 100 毫米之间的射野大小。基于 TPS 计算或测量数据集，分别生成了 ML 回归模型。在不同射野大小（FS）下，对小射野 OFs 进行了准确预测，与机架和 MLC 位置无关。基于测量数据集的最佳模型的平均和最大相对误差分别为 0.38 ± 0.39%和 3.62%。预测精度与叶片末端透射的贡献无关。针对预测小射野 OFs 生成、验证和测试了几种 ML 模型。将这些模型纳入剂量计算工作流程，可以大大提高任何严重依赖小射野的放射治疗技术的剂量计算的准确性和稳健性。
