Abstract
Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.
| Original language | English |
|---|---|
| Pages (from-to) | 35-38 |
| Number of pages | 4 |
| Journal | Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics |
| Volume | 36 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 1 2016 |
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All Science Journal Classification (ASJC) codes
- Medicine(all)
Cite this
Applications of Machine Learning for Radiation Therapy. / Arimura, Hidetaka; Nakamoto, Takahiro.
In: Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics, Vol. 36, No. 1, 01.01.2016, p. 35-38.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Applications of Machine Learning for Radiation Therapy
AU - Arimura, Hidetaka
AU - Nakamoto, Takahiro
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.
AB - Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.
UR - http://www.scopus.com/inward/record.url?scp=85021308488&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021308488&partnerID=8YFLogxK
U2 - 10.11323/jjmp.36.1_35
DO - 10.11323/jjmp.36.1_35
M3 - Review article
C2 - 28428495
AN - SCOPUS:85021308488
VL - 36
SP - 35
EP - 38
JO - Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics
JF - Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics
SN - 1345-5354
IS - 1
ER -