Modeling of automated planning for esophageal cancer under complex dose prescription and anatomical sections
Chen Yan, Wang Haiyang, Liu Hongjia, Wang Meijiao, Han Jianjun, He Jun, Jia Dong, Li Sha, Wu Hao, Pu Yichen, Zhang Yibao
Abstract
ObjectiveTo study the feasibility and dosimetric characteristics of establishing a comprehensive model for automated treatment planning for esophageal cancer based on Varian RapidPlan module under complex conditions such as different prescriptions and anatomical sections.
MethodsIn total, 301 historical plans with multi-prescription and multi-sectional esophageal cancer were imported into RapidPlan system. Assisted by the ModelAnalytics(MA) tool, statistical verification was performed to profess outliers, yielding the initial model; Additional 40 clinical esophageal cancer treatment plans were duplicated as validation set. The RapidPlan-based re-optimization result was assessed and used as feedback data to fine-tune the model parameters iteratively. The primary dosimetric parameters of the two groups were then compared.
ResultsThrough enlarged training set sample size and structure matching (based on relative dose rather than nomenclature), a comprehensive model feasible of handling various anatomic sections and dose prescriptions was successfully established. Both clinical plans and RapidPlan re-optimization were clinically acceptable, displaying complementary dosimetric advantages. Compared with the trial-and-error process of conventional manual planning, RapidPlan method was more efficient and independent from subjective influence, which induced inconsistency of plan quality.
ConclusionsThis work proposed and validated a modeling method of automated treatment planning for esophageal cancer under complex anatomic section and dose prescription. Dosimetric performance of the model is assessed based on independent validation set.
Key words:
Esophageal cancer; Radiotherapy; Rapidplan model; Automated treatment planning
Contributor Information
Chen Yan
Department of Radiation Oncology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang 621000, China
Wang Haiyang
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital &
Institute, Beijing 100142, China
Liu Hongjia
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital &
Institute, Beijing 100142, China
Wang Meijiao
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital &
Institute, Beijing 100142, China
Han Jianjun
Department of Radiation Oncology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang 621000, China
He Jun
Department of Radiation Oncology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang 621000, China
Jia Dong
Department of Radiation Oncology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang 621000, China
Li Sha
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital &
Institute, Beijing 100142, China
Wu Hao
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital &
Institute, Beijing 100142, China
Pu Yichen
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital &
Institute, Beijing 100142, China
Zhang Yibao
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital &
Institute, Beijing 100142, China
