A statistical approach for achievable dose querying in IMRT planning.
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.
Simari
Patricio
P
Department of Computer Science, Johns Hopkins University, USA.
Wu
Binbin
B
Jacques
Robert
R
King
Alex
A
McNutt
Todd
T
Taylor
Russell
R
Kazhdan
Michael
M
D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D014915:Whole-Body Counting / Q000379:methods*