Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT

作者：Yongbao Li, Wenwen Cai, Fan Xiao, Xuanru Zhou, Jiajun Cai, Linghong Zhou, Wen Dou, Ting Song · 发表于：Radiation Oncology · 年份：2023 · DOI：10.1186/s13014-023-02287-4 · 被引用次数：14 · 研究领域：Advanced Radiotherapy Techniques、Head and Neck Cancer Studies、Radiomics and Machine Learning in Medical Imaging

BACKGROUND: Current intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively. METHODS: We proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body. RESULTS: The proposed network successfully predicted similar dose distribution and fluence maps comp...