Reduced-order parameter optimization for simplifying prostate IMRT planning.
Intensity-modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time manually adjusting IMRT optimization parameters such as dose limits and costlet weights in order to obtain a clinically acceptable plan. In this paper, we describe two main advances that simplify the parameter adjustment process for five-field prostate IMRT planning. First, we report the results of a sensitivity analysis that quantifies the effect of each hand-tunable parameter of the IMRT cost function on each clinical objective and the overall quality of the resulting plan. Second, we show that a recursive random search over the six most sensitive parameters as an outer loop in IMRT planning can quickly and automatically determine parameters for the cost function that lead to a plan meeting the clinical requirements. Our experiments on a ten-patient dataset show that for 70% of the cases, we can automatically determine a plan in 10 min (on the average) that is either clinically acceptable or requires only minor adjustment by the planner. The outer-loop optimization can be easily integrated into a traditional IMRT planning system.
Lu
Renzhi
R
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. lur@rpi.edu
Radke
Richard J
RJ
Happersett
Laura
L
Yang
Jie
J
Chui
Chen-Shou
CS
Yorke
Ellen
E
Jackson
Andrew
A
D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011880:Radiotherapy Planning, Computer-Assisted / Q000706:statistics & numerical data*; D050397:Radiotherapy, Intensity-Modulated / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity