Utilizing Data-Mining and Deep Learning Methods on Retrospective Head and Neck Radiation Therapy Cases for Decision Support of Individualized Treatment Planning

While there are formal guidelines and target dose levels used in treatment planning for radiation therapy, currently plans are created to adhere to these goals based on physician experience and trial-and-error rather than standard quantitative methods. To introduce uniformity and data driven methods into the radiation therapy treatment planning process we create a web based informatics application which uses algorithmic analysis of historical cases to identify and provide treatment plan templates and treatment benchmarking. The system relies on a database of 360 historical DICOM RT objects from University of California Los Angeles and State University of New York Buffalo; Roswell Park as well as the quantitative features we calculate from each case. To quantitatively identify each case we calculated the overlap volume histogram and spatial target similarity in our feature extraction algorithms. A case undergoing treatment planning when uploaded to our web application will have it's quantitative features automatically extracted and then our similarity matching algorithm which matches cases based on the similarity/dissimilarity of their quantitative features is used to generate a list of similar historical cases from our database which a physician can then use for reference. Our database also stores treatment outcomes which we will use to establish relationships between the anatomy of the tumor and surrounding organs, the treatment and outcome. These identified relationships will be used in benchmarking and treatment plan assessment. The system aims to increase uniformity of methods and introduce data driven practices into radiation therapy treatment planning.