Implement a knowledge-based automated dose volume histogram prediction module in Pinnacle3 treatment planning system for plan quality assurance and guidance

Abstract

Purpose: This work aims to develop a knowledge-based automated dose volume histogram (DVH) prediction module that serves as a plan quality evaluation tool and treatment planning guidance in commercial Pinnacle3 treatment planning system (Philips Radiation Oncology Systems, Fitchburg, WI, USA). Methods: The knowledge-based automated DVH prediction module was developed with kernel density estimation (KDE) method and applied for Pinnacle3 treatment planning system. Treatment plan data from 20 esophageal cancer cases were used for creating a module to predict DVHs. Twenty additional esophageal clinical plans were evaluated on the developed module. Predicted DVHs were compared with manual ones. Differences between the predicted and achieved DVHs were analyzed. Results: The plan evaluation module was successfully implemented in Pinnacle3 treatment planning system. Strong linear correlations were found between predicted and achieved DVH for organs at risk. Suboptimal treatment plan quality could be improved according to the predicted DVHs by the module. Conclusion: The knowledge-based automated DVH prediction module implemented in Pinnacle3 could be used to efficiently evaluate the treatment plan quality and as guidance for further plan optimization.