Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy.
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 adjusting IMRT optimization parameters in order to get a clinically acceptable plan. We demonstrate that the relationship between patient geometry and radiation intensity distributions can be automatically inferred using a variety of machine learning techniques in the case of two-field breast IMRT. Our experiments show that given a small number of human-expert-generated clinically acceptable plans, the machine learning predictions produce equally acceptable plans in a matter of seconds. The machine learning approach has the potential for greater benefits in sites where the IMRT planning process is more challenging or tedious.
Lu
Renzhi
R
Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute. Troy, NY 12180 USA. lur@rpi.edu
Radke
Richard J
RJ
Hong
Linda
L
Chui
Chen-Shou
CS
Xiong
Jianping
J
Yorke
Ellen
E
Jackson
Andrew
A