Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis.
Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, following the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining 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.
Matthew
M
Foote
Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia; School of Medicine, University of Queensland, Australia.
Margot
M
Lehman
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
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000175:diagnosis / Q000532:radiotherapy* / Q000556:secondary*; D003198:Computer Simulation; D016371:Cranial Irradiation / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D011874:Radiometry / Q000379:methods; D016634:Radiosurgery; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
Brain metastasis; Optimization; Stereotactic radiotherapy; Vector model; Volumetric modulated