Deep learning-based fluence map prediction(DL-FMP) method has been reported in the literature, which generated fluence maps for desired dose by deep neural network(DNN)-based inverse mapping. We hypothesized that DL-FMP is similar to general fluence map optimization(FMO) because it's theoretically based on a general inverse mapping. We designed four experiments to validate the generalizability of DL-FMP to other types of plans apart from the training data, which contained only clinical head and neck(HN) full-arc VMAT plans. The first three experiments quantified the generalizability of DL-FMP to multiple anatomical sites, different delivery modalities, and various degree of modulation(DOM), respectively. The fourth experiment explored the generalizability and stability to infeasible dose inputs. Results of the first experiment manifested that DL-FMP can generalize to lung, liver, esophagus and prostate, with gamma passing rates (GPR) higher than 95%(2%/2mm). The second experiment showed that DL-FMP can generalize to partial-arc plans and predict partial-arc fluences. GPR(3mm/3%) ranged from 96% to 99%. DL-FMP cannot generate fluence maps in discrete beam angles for IMRT input. But the predicted dose still agreed with ground truth dose with 93% GPR(5%/5mm). The third experiment demonstrated that DL-FMP can generalize to various DOMs, with GPRs(3%/3mm) ranged in 94%-98%. Moreover, the DOM of predicted fluence maps correlated to the optimality of the input dose accordingly. The fourth experiment exemplified that DL-FMP can make stable predictions for infeasible dose input. In conclusion, we validated that DL-FMP can generalize to plans for multiple anatomical sites, plans of different delivery modalities and plans with various DOM. It can also make stable prediction for infeasible input.
Mayo Clinic（梅奥诊所）；University of Georgia（佐治亚大学）；Technische Universität Dresden（德累斯顿工业大学）；Helmholtz-Zentrum Dresden - Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；University of Virginia（弗吉尼亚大学）
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy
Department of Radiation Oncology, Columbia University Irving Medical Center（放射肿瘤学系，哥伦比亚大学伊文思医疗中心）；Herbert Irving Comprehensive Cancer Center, Columbia University（赫伯特·伊文思综合癌症中心，哥伦比亚大学）；Data Science Institute, Columbia University（数据科学研究所，哥伦比亚大学）
Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy
Department of Medical Physics, Memorial Sloan Kettering Cancer Center（医学物理系，纪念斯隆-凯特琳癌症中心）；Department of Radiation Oncology and Winship Cancer Institute, Emory University（放射肿瘤学系和Winship癌症研究所，埃默里大学）；Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine（Wallace H. Coulter生物医学工程系，佐治亚理工学院和埃默里大学医学院）；Department of Biomedical Informatics, Emory University（生物医学信息学系，埃默里大学）
