Robustness comparative study of dose–volume–histogram prediction models for knowledge-based radiotherapy treatment planning. Issue 1 (1st January 2020) - Wu, Aiqian; Li, Yongbao; Qi, Mengke; Jia, Qiyuan; Guo, Futong; Lu, Xingyu; Zhou, Linghong; Song, Ting
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.