Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy Journal Article


Authors: Lu, R.; Radke, R. J.; Hong, L.; Chui, C. S.; Xiong, J.; Yorke, E.; Jackson, A.
Article 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. © 2006 IEEE.
Keywords: clinical article; intensity modulated radiation therapy; treatment planning; breast cancer; radiotherapy dosage; radiotherapy; breast neoplasms; prediction; algorithm; imrt; intensity-modulated radiotherapy; tumors; geometry; artificial intelligence; radiometry; radiotherapy planning, computer-assisted; radiotherapy, conformal; relative biological effectiveness; radiation beam; body burden; computer assisted radiotherapy; radiation dose distribution; biological radiation effects; therapy, computer-assisted; calculation; learning; decision support techniques; optimization; mathematical analysis; decision support systems, clinical; apparatus; machine learning; learning systems; radiation depth dose; optimization parameters; radiation intensity; computational geometry; machine
Journal Title: IEEE Transactions on Biomedical Engineering
Volume: 53
Issue: 5
ISSN: 0018-9294
Publisher: IEEE  
Date Published: 2006-05-01
Start Page: 908
End Page: 920
Language: English
DOI: 10.1109/tbme.2005.863987
PUBMED: 16686413
PROVIDER: scopus
DOI/URL:
Notes: --- - "Export Date: 4 June 2012" - "Art. No.: 1621142" - "CODEN: IEBEA" - "Source: Scopus"
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Linda Xueqi Hong
    88 Hong
  2. Andrew Jackson
    253 Jackson
  3. Ellen D Yorke
    450 Yorke
  4. Jianping Xiong
    23 Xiong
  5. Chen Chui
    144 Chui