Creating a Data Science Platform for Developing Complication Risk Models for Personalized Treatment Planning in Radiation Oncology

The common approach to assessing risk in radiation oncology treatment uses Lyman-Kutcher-Burman (LKB) derived models to calculate normal tissue complication probability (NTCP). LKB is not sufficiently robust to capture the modern clinical reality of three-dimensional intensity modulated radiation therapy (IMRT) treatments, the approach accounts for only two factors -- Dmax and Veff. We present a data science platform designed to facilitate the rapid creation of data-derived NTCP models. The platform extracts native Philips Pinnacle data such as dose grids and contoured regions using the cross-vendor DICOM RT standard. Further, outcome data is encoded using Common Terminology Criteria for Adverse Events 4.0. Thus, the platform exploits the normal clinical workflow and information encoded with a standard ontology. Over the course of less than three weeks we used the platform to create NTCP models for two complications (xerostomia and voice dysfunction due to parotid and larynx irradiation, respectively). We assess the resulting platform with a focus on its context within a Learning Health System (LHS). We believe that the system reported can serve as a guide to the development of radiation oncology data science platforms in particular and local-level LHS components in general.