A statistical approach for achievable dose querying in IMRT planning

Authors:
Patricio Simari;Binbin Wu;Robert Jacques;Alex King;Todd McNutt;Russell Taylor;Michael Kazhdan
Affiliations:
Department of Computer Science, Johns Hopkins University;Department of Radiation Oncology and Molecular Radiation Science, Johns Hopkins University;Department of Biomedical Engineering, Johns Hopkins University;Department of Computer Science, Johns Hopkins University;Department of Radiation Oncology and Molecular Radiation Science, Johns Hopkins University;Department of Computer Science, Johns Hopkins University;Department of Computer Science, Johns Hopkins University
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

The task of IMRT planning, particularly in head-and-neck cancer, is a difficult one, often requiring days of work from a trained dosimetrist. One of the main challenges is the prescription of achievable target doses that will be used to optimize a treatment plan. This work explores a data-driven approach in which effort spent on past plans is used to assist in the planning of new patients. Using a database of treated patients, we identify the features of patient geometry that are correlated with received dose and use these to prescribe target dose levels for new patients. We incorporate our approach in a quality-control system, identifying patients with organs that received a dose significantly higher than the one recommended by our method. For all these patients, we have found that a replan using our predicted dose results in noticeable sparing of the organ without compromising dose to other treatment volumes.