Vector-model-supported approach in prostate plan optimization.
Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.
Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
Eva Sau Fan
ESF
Liu
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
Vincent Wing Cheung
VWC
Wu
Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia.
Benjamin
B
Harris
Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia; School of Medicine, University of Queensland, Australia.
Margot
M
Lehman
Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia; School of Medicine, University of Queensland, Australia.
David
D
Pryor
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong. Electronic address: wing.chi.chan@polyu.edu.hk.
Lawrence Wing Chi
LWC
Chan
D003198:Computer Simulation; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D006801:Humans; D008297:Male; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
Intensity-modulated radiotherapy; Optimization; Prostate cancer; Vector model; Volumetric-modulated radiotherapy