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Updated: Mar 22, 2026
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
Published on: April 11, 2018
Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer
Ruchi R Deshpande1, John DeMarco2, James W Sayre3
1Image Processing and Informatics Laboratory, Denney Research Center (DRB) 264 1042 Downey Way, University of Southern California, Los Angeles, CA, 90089-1111, USA. ruchi.deshpande@gmail.com.
Purpose:
Clinical data that are generated through routine radiation therapy procedures can be leveraged as a source of knowledge to provide evidence-based decision support for future patients. Treatment planning in radiation therapy often relies on trial-and-error iterations, experience, judgment calls and general guidelines. The authors present a knowledge-driven decision support system that assists clinicians by reducing some of the uncertainties associated with treatment planning and provides quantified empirical estimates to help minimize the radiation dose to healthy critical structures surrounding the tumor.
Methods:
A database of retrospective DICOM RT data fuels a decision support engine, which assists clinicians in selecting dose constraints and assessing dose distributions. The first step is to quantify the spatial relationships between the tumor and surrounding critical structures through features that account for distance, volume, overlap, location, shape and orientation. These features are used to identify database cases that are anatomically similar to the new patient. The dose profiles of these database cases can help clinicians to estimate an acceptable dose distribution for the new case, based on empirical evidence. Since database diversity is essential for good system performance, an infrastructure for multi-institutional collaboration was also conceptualized in order to pave the way for data sharing of protected health information.
Results:
A set of 127 retrospective test cases was collected from a single institution in order to conduct a leave-one-out evaluation of the decision support module. In 72 % of these retrospective test cases, patients with similar tumor anatomy were also found to exhibit similar radiation dose distributions. This demonstrates the system's ability to successfully extract retrospective database cases that can estimate the new patient's dose distribution.
Conclusion:
The radiation therapy treatment planning decision support system presented here can assist clinicians in determining good dose constraints and assessing dose distributions by using knowledge gained from retrospective treatment plans.
