title: Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy.
abstract: 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.
authors: Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute. Troy, NY 12180 USA. lur@rpi.edu
authors: Renzhi
authors: R
authors: Lu
authors: Richard J
authors: RJ
authors: Radke
authors: Linda
authors: L
authors: Hong
authors: Chen-Shou
authors: CS
authors: Chui
authors: Jianping
authors: J
authors: Xiong
authors: Ellen
authors: E
authors: Yorke
authors: Andrew
authors: A
authors: Jackson