TY - JOUR
T1 - Automation in radiotherapy treatment planning
T2 - Examples of use in clinical practice and future trends for a complete automated workflow
AU - Meyer, P.
AU - Biston, M. C.
AU - Khamphan, C.
AU - Marghani, T.
AU - Mazurier, J.
AU - Bodez, V.
AU - Fezzani, L.
AU - Rigaud, P. A.
AU - Sidorski, G.
AU - Simon, L.
AU - Robert, C.
N1 - Publisher Copyright:
© 2021 Société française de radiothérapie oncologique (SFRO)
PY - 2021/10/1
Y1 - 2021/10/1
N2 - Modern radiotherapy treatment planning is a complex and time-consuming process that requires the skills of experienced users to obtain quality plans. Since the early 2000s, the automation of this planning process has become an important research topic in radiotherapy. Today, the first commercial automated treatment planning solutions are available and implemented in a growing number of clinical radiotherapy departments. It should be noted that these various commercial solutions are based on very different methods, implying a daily practice that varies from one center to another. It is likely that this change in planning practices is still in its infancy. Indeed, the rise of artificial intelligence methods, based in particular on deep learning, has recently revived research interest in this subject. The numerous articles currently being published announce a lasting and profound transformation of radiotherapy planning practices in the years to come. From this perspective, an evolution of initial training for clinical teams and the drafting of new quality assurance recommendations is desirable.
AB - Modern radiotherapy treatment planning is a complex and time-consuming process that requires the skills of experienced users to obtain quality plans. Since the early 2000s, the automation of this planning process has become an important research topic in radiotherapy. Today, the first commercial automated treatment planning solutions are available and implemented in a growing number of clinical radiotherapy departments. It should be noted that these various commercial solutions are based on very different methods, implying a daily practice that varies from one center to another. It is likely that this change in planning practices is still in its infancy. Indeed, the rise of artificial intelligence methods, based in particular on deep learning, has recently revived research interest in this subject. The numerous articles currently being published announce a lasting and profound transformation of radiotherapy planning practices in the years to come. From this perspective, an evolution of initial training for clinical teams and the drafting of new quality assurance recommendations is desirable.
KW - Automated radiotherapy treatment planning
KW - Dose mimicking
KW - Dose prediction
UR - http://www.scopus.com/inward/record.url?scp=85108532918&partnerID=8YFLogxK
U2 - 10.1016/j.canrad.2021.06.006
DO - 10.1016/j.canrad.2021.06.006
M3 - Short survey
C2 - 34175222
AN - SCOPUS:85108532918
SN - 1278-3218
VL - 25
SP - 617
EP - 622
JO - Cancer/Radiotherapie
JF - Cancer/Radiotherapie
IS - 6-7
ER -