ABSTRACT

Purpose: To compare the robustness of three dose-volume-histogram (DVH) prediction models for knowledge-based treatment planning (KBP) for radiation therapy.

Methods: Three models proposed by Zhu et al. (Zmodel), Lindsey et al. (Lmodel), and Satomi et al. (Smodel) were selected, and compared based on identified archived radiation therapy plan cohorts (including 50 prostate cancer (PC) and 29 nasopharynx cancer (NPC) cases). Robustness comparison was performed by observing changes in prediction accuracy in relation to training example size, and further analyzing the number of training samples required for each model. In addition, a robustness comparison of models on different case applications was conducted to verify the applicability of models on different tumor sites. The error of model predictions was measured by the difference between predicted and clinical DVH.

Results: The minimum necessary datasets required to train the model are 35, 40, and 45 for Lmodel, Zmodel, and Smodel, respectively. Smodel has high accuracy on both PC and NPC databases, achieving a median prediction error of 0.0257 on the training dataset and 0.0446 on the evaluation dataset. In a specific case, Smodel and Zmodel exhibit the best result on PC (with prediction errors of 0.0464) and NPC case applications (with prediction errors of 0.0228), respectively.

Conclusions: Lmodel needs the least number of samples necessary for training. Smodel and Zmodel are optimal for the PC and NPC cases, respectively. In different case applications, Smodel performs more stable. Planners or researchers should carefully select an appropriate method under specific requirements.

摘要

目的：比较三种剂量-体积-直方图（DVH）预测模型在基于知识的放射治疗计划（KBP）中的鲁棒性。

方法：Zhu等人提出的三种模型。（Zmodel），Lindsey等。（Lmodel）和Satomi等人。选择（Smodel），并根据已确定的存档放射治疗计划队列（包括50例前列腺癌（PC）和29例鼻咽癌（NPC）病例）进行比较。通过观察与训练样本大小有关的预测准确性的变化，并进一步分析每种模型所需的训练样本数量，来进行鲁棒性比较。此外，对不同病例应用的模型进行了鲁棒性比较，以验证模型在不同肿瘤部位的适用性。模型预测的误差是通过预测DVH与临床DVH之间的差异来衡量的。

结果：训练Lmodel，Zmodel和Smodel所需的最小必要数据集分别为35、40和45。Smodel在PC和NPC数据库上均具有较高的准确性，在训练数据集上的平均预测误差为0.0257，在评估数据集上的平均预测误差为0.0446。在特定情况下，Smodel和Zmodel分别在PC（预测误差为0.0464）和NPC案例应用（预测误差为0.0228）上显示最佳结果。

结论：Lmodel需要最少数量的训练样本。Smodel和Zmodel分别是PC和NPC的最佳选择。在不同的情况下，Smodel的性能更稳定。计划者或研究者应根据特定要求仔细选择合适的方法。