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J Appl Clin Med Phys. 2018 Sep;19(5):491-498. doi: 10.1002/acm2.12403. Epub 2018 Jul 8.

An interactive plan and model evolution method for knowledge-based pelvic VMAT planning.

Author information

1
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, China.
2
Department of Medical Physics, Institute of Medical Humanities, Peking University, Beijing, China.
3
Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Institute of Heavy Ion Physics, Peking University, Beijing, China.

Abstract

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 (P0 ) that were used to configure an initial rectal RapidPlan model (M0 ) were reoptimized using M0 (closed-loop), yielding 81 P1 plans. The 75 improved P1 (P1+ ) and the remaining 6 P0 were used to configure model M1 . The 81 training plans were reoptimized again using M1 , producing 23 P2 plans that were superior to both their P0 and P1 forms (P2+ ). Hence, the knowledge base of model M2 composed of 6 P0 , 52 P1+ , and 23 P2+ . Models were tested dosimetrically on 30 VMAT validation cases (Pv ) that were not used for training, yielding Pv (M0 ), Pv (M1 ), and Pv (M2 ) respectively. The 30 Pv were also optimized by M2_new as trained by the library of M2 and 30 Pv (M0 ).

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 M1 (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M2 (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M0 . However, mean dose to femoral head increased by 0.81 Gy/6.64% (M1 ) and 0.91 Gy/7.46% (M2 ) than using M0 . The overfitting problem was relieved by applying model M2_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.

KEYWORDS:

RapidPlan; knowledge-based planning; model improvement; rectal cancer

PMID:
29984464
PMCID:
PMC6123168
DOI:
10.1002/acm2.12403
[Indexed for MEDLINE]
Free PMC Article

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