Head and neck cancer radiation therapy decision support

Trent Benedick; Veda Murthy; Rhea Koparde; Maying Shi; Sabrina Lieu; Siliang Zhang; Benjamin Yao; Anh Le; Brent J. Liu

doi:10.1117/12.2513075

15 March 2019 Head and neck cancer radiation therapy decision support

Trent Benedick, Veda Murthy, Rhea Koparde, Maying Shi, Sabrina Lieu, Siliang Zhang, Benjamin Yao, Anh Le, Brent J. Liu

Proceedings Volume 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications; 109540Y (2019) https://doi.org/10.1117/12.2513075
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Currently the methods used to develop radiation therapy treatment plans for head and neck cancers rely on clinician experience and a small set of universal guidelines which result in inconsistent and variable methods. Data driven support can provide assistance to clinicians by reducing inconsistency associated with treatment planning and provide empirical estimates to minimize the radiation to healthy organs near the tumor. We created a database of DICOM RT objects which stores historical cases and when a new DICOM object is uploaded it will return a set of similar treatment plans to assist the clinician in creating the treatment plan for the current patient. The database works first by extracting features from DICOM RT object to quantitatively compare and evaluate the similarity of cases enabling the system to mine for cases with defined similarity. The feature extraction methods are based on the spatial relationships between the tumors and organs at risk which allows the generation the overlap volume histogram and spatial target similarity which demonstrate the volumetric and locational similarity between the organ at risk and the tumor. It is useful to find cases with similar tumor anatomy because this similarity translates to similarity in radiation dosage. The developed system was applied to three different RT sites, University of California Los Angeles, Technical University at Munich and State University of New York Buffalo; Roswell Park, with a total of 247 cases to evaluate the system for both inter- and intra- institutional best practices and results. Future roadmap will be discussed for correlating outcomes results to the decision support system which will enhance the overall performance and utilization of the decision support system in the RT workflow. In the future, because this database returns similar historical cases to a current one this could be a worthwhile decision support tool for clinicians as they create new radiation therapy treatment plans.

Conference Presentation

Citation Download Citation

Trent Benedick, Veda Murthy, Rhea Koparde, Maying Shi, Sabrina Lieu, Siliang Zhang, Benjamin Yao, Anh Le, and Brent J. Liu "Head and neck cancer radiation therapy decision support", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540Y (15 March 2019); https://doi.org/10.1117/12.2513075

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