1Department of Radiation Oncology,The First Affiliated Hospital,University of Science and Technology of China West District,Hefei 230001,China; 2National Synchrotron Radiation Laboratory,University of Science and Technology of China,Hefei 230001,China; 3Department of Engineering and Applied Physics, School of Physical Science, University of Science and Technology of China, Hefei 230001 China
Abstract：Objective To evaluate the feasibility of utilizing dose-volume histogram (DVH) prediction models of organs at risk (OARs) to deliver automatic treatment planning of prostate cancer. Methods The training set included 30 cases randomly selected from a database of 42 cases of prostate cancer receiving treatment planning. The bladder and rectum were divided into sub-volumes (Ai) of 3 mm in layer thickness according to the spatial distance from the boundary of planning target volume (PTV). A skewed normal Gaussian function was adopted to fit the differential DVH of Ai,and a precise mathematical model was built after optimization. Using the embedded C++ subroutine of Pinnacle script,the volume of each Ai of the remaining validation set for 12 patients was obtained to predict the DVH parameters of these OARs,which were used as the objective functions to create personalized Pinnacle script. Finally,automatic plans were generated using the script. The dosimetric differences among the original clinical planning,predicted value and the automatic treatment planning were statistically compared with paired t-test. Results DVH residual analysis demonstrated that predictive volume fraction of the bladder and rectum above 6 000 cGy were lower than those of the original clinical planning. The automatic treatment planning significantly reduced the V70,V60,V50 of the bladder and the V70 and V60 of the rectum than the original clinical planning (all P<0.05),the coverage and conformal index (CI) of PTV remained unchanged,and the homogeneity index (HI) was slightly decreased with no statistical significance (P>0.05). Conclusion The automatic treatment planning of the prostate cancer based on the DVH prediction models can reduce the irradiation dose of OARs and improve the treatment planning efficiency.
Zhou Jieping,Peng Zhao,Song Yuchen et al. The study of automatic treatment planning of prostate cancer based on DVH prediction models of organs at risk[J]. Chinese Journal of Radiation Oncology, 2019, 28(7): 536-542.
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2019, Vol. 28 
