Open AccessAn interactive plan and model evolution method for knowledge‐based pelvic VMAT planning
Author(s) -
Wang Meijiao,
Li Sha,
Huang Yuliang,
Yue Haizhen,
Li Tian,
Wu Hao,
Gao Song,
Zhang Yibao
Publication year - 2018
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12403
Subject(s) - nuclear medicine , medicine , mathematics
Purpose To test if a RapidPlan DVH estimation model and its training plans can be improved interactively through a closed‐loop evolution process. Methods and materials Eighty‐one manual plans (P 0 ) that were used to configure an initial rectal RapidPlan model (M 0 ) were reoptimized using M 0 (closed‐loop), yielding 81 P 1 plans. The 75 improved P 1 (P 1+ ) and the remaining 6 P 0 were used to configure model M 1 . The 81 training plans were reoptimized again using M 1 , producing 23 P 2 plans that were superior to both their P 0 and P 1 forms (P 2+ ). Hence, the knowledge base of model M 2 composed of 6 P 0 , 52 P 1+ , and 23 P 2+ . Models were tested dosimetrically on 30 VMAT validation cases (P v ) that were not used for training, yielding P v (M 0 ), P v (M 1 ), and P v (M 2 ) respectively. The 30 P v were also optimized by M 2_new as trained by the library of M 2 and 30 P v (M 0 ). Results Based on comparable target dose coverage, the first closed‐loop reoptimization significantly ( P < 0.01) reduced the 81 training plans’ mean dose to femoral head, urinary bladder, and small bowel by 2.65 Gy/15.63%, 2.06 Gy/8.11%, and 1.47 Gy/6.31% respectively, which were further reduced significantly ( P < 0.01) in the second closed‐loop reoptimization by 0.04 Gy/0.28%, 0.18 Gy/0.77%, 0.22 Gy/1.01% respectively. However, open‐loop VMAT validations displayed more complex and intertwined plan quality changes: mean dose to urinary bladder and small bowel decreased monotonically using M 1 (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M 2 (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M 0 . However, mean dose to femoral head increased by 0.81 Gy/6.64% (M 1 ) and 0.91 Gy/7.46% (M 2 ) than using M 0 . The overfitting problem was relieved by applying model M 2_new . Conclusions The RapidPlan model and its constituent plans can improve each other interactively through a closed‐loop evolution process. Incorporating new patients into the original training library can improve the RapidPlan model and the upcoming plans interactively.