Creating a data science platform for developing complication risk models for personalized treatment planning in radiation oncology

Fumbeya Marungo, Scott Robertson, Harry Quon, John Rhee, Hilary Paisley, Russell H. Taylor, Todd McNutt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 48th Annual Hawaii International Conference on System Sciences, HICSS 2015
EditorsRalph H. Sprague, Tung X. Bui
PublisherIEEE Computer Society
Pages3132-3140
Number of pages9
ISBN (Electronic)9781479973675
DOIs
StatePublished - Mar 26 2015
Event48th Annual Hawaii International Conference on System Sciences, HICSS 2015 - Kauai, United States
Duration: Jan 5 2015Jan 8 2015

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2015-March
ISSN (Print)1530-1605

Other

Other48th Annual Hawaii International Conference on System Sciences, HICSS 2015
CountryUnited States
CityKauai
Period1/5/151/8/15

ASJC Scopus subject areas

  • Engineering(all)

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    Marungo, F., Robertson, S., Quon, H., Rhee, J., Paisley, H., Taylor, R. H., & McNutt, T. (2015). Creating a data science platform for developing complication risk models for personalized treatment planning in radiation oncology. In R. H. Sprague, & T. X. Bui (Eds.), Proceedings of the 48th Annual Hawaii International Conference on System Sciences, HICSS 2015 (pp. 3132-3140). [7070193] (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 2015-March). IEEE Computer Society. https://doi.org/10.1109/HICSS.2015.378