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In vivo 3D dose distribution verification for lung cancer:from rigid-body model to porcine lung

摘要This study introduces a novel concept,biological in vivo three-dimensional(3D)dose distribution verification,aimed at inves-tigating how respiratory motion affects the efficacy of lung cancer radiotherapy,representing an evolution from the current standard of rigid-body dose distribution verification.A 3D ex vivo biological lung motion simulation device(3D-BioLungEx)was designed to replicate human respiration.A radiotherapy plan of the patient was copied to the porcine lung,which was driven by 3D-BioLungEx to simulate various respiratory patterns that occur during treatment.To ensure anatomical consis-tency with the patient's lung structure,during transmission,skin,skeleton,and organs were adjusted according to CT images of the porcine lung.The patient's radiotherapy plan was then adapted to the porcine lung using the Monaco treatment plan-ning system(TPS).Next,an iterative optimization and scatter inversion-based dose distribution retro-analysis algorithm(IOSI-BLDose)was developed to calculate the dose distribution during treatment.Gamma passing rates were used to quantify discrepancies between this dose distribution and that of the radiotherapy plan.When respiratory conditions were replicated,the passing rate reached up to 93.61%,while irregular breathing dropped it to 70%-90%,primarily due to amplitude changes.However,cycle variations had minimal impact.Compared to conventional rigid-body dose distribution verification,our method provides real-time biological feedback and more effectively captures motion-induced deviations.Accordingly,our biological in vivo 3D dose distribution verification has potential for improving treatment precision and enabling adaptive ra-diotherapy in clinical practice.

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作者 Yutao Zhang [1] Kai Xie [1] Lintao Song [1] Jiewei Lai [2] Haiping Zheng [3] Qianjia Huang [4] Hao Wang [4] Tao Lin [1] Liugang Gao [1] Jiawei Sun [1] Jianrong Dai [5] Xinye Ni [1] 学术成果认领
作者单位 Department of Radiotherapy,The Second People's Hospital of Changzhou,The Third Affiliated Hospital of Nanjing Medical University,Changzhou 213003,China;Jiangsu Province Engineering Research Center of Medical Physics,Changzhou 213003,China;Center for Medical Physics,Nanjing Medical University,Changzhou 213003,China;Changzhou Key Laboratory of Medical Physics,Changzhou 213003,China [1] School of Biomedical Engineering and Informatics,Nanjing Medical University,Nanjing 211166,China [2] Department of Research and Development,Guangzhou Raydose Medical Technology Co.,Ltd.,Guangzhou 510760,China [3] School of Computer Science and Artificial Intelligence,Changzhou University,Changzhou 213159,China [4] Department of Radiation Oncology,Cancer Hospital Chinese Academy of Medical Sciences,Beijing 100020,China [5]
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DOI 10.1631/bdm.2500077
发布时间 2025-12-09(万方平台首次上网日期,不代表论文的发表时间)
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