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" |
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