Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations
Abstract Background Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic pl...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | Radiation Oncology |
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| Online Access: | https://doi.org/10.1186/s13014-024-02531-5 |
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| author | Yuchao Miao Jiwei Li Ruigang Ge Chuanbin Xie Yaoying Liu Gaolong Zhang Mingchang Miao Shouping Xu |
| author_facet | Yuchao Miao Jiwei Li Ruigang Ge Chuanbin Xie Yaoying Liu Gaolong Zhang Mingchang Miao Shouping Xu |
| author_sort | Yuchao Miao |
| collection | DOAJ |
| description | Abstract Background Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients. This study seeks to develop a more versatile model incorporating variable beam configurations of CK and considering the patient’s anatomy. Methods This study proposed that the AB (anatomy and beam) model be compared with the control Mask (only anatomy) model. These models are based on a 3D U-Net network to investigate the impact of CK beam encoding information on dose prediction. The study collected 86 lung cancer patients who received CK′s built-in MC algorithm plans using different beam configurations for training/validation (66 cases) and testing (20 cases). We compared the gamma passing rate, dose difference maps, and relevant dose-volume metrics to evaluate the model’s performance. In addition, the Dice similarity coefficients (DSCs) were calculated to assess the spatial correspondence of isodose volumes. Results The AB model demonstrated superior performance compared to the Mask model, particularly in the trajectory dose of the beam. The DSCs of the AB model were 20–40% higher than that of the Mask model in some dose regions. We achieved approximately 99% for the PTV and generally more than 95% for the organs at risk (OARs) referred to the clinical planning dose in the gamma passing rates (3 mm/3%). Relative to the Mask model, the AB model exhibited more than 90% improvement in small voxels (p < 0.001). The AB model matched well with the clinical plan’s dose-volume histograms, and the average dose error for all organs was 1.65 ± 0.69%. Conclusions Our proposed new model signifies a crucial advancement in predicting CK 3D dose distributions for clinical applications. It enables planners to rapidly and precisely predict MC doses for lung cancer based on patient-specific beam configurations and optimize the CK treatment process. |
| format | Article |
| id | doaj-art-84bbbacda5d84b0384a2203aee292961 |
| institution | OA Journals |
| issn | 1748-717X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj-art-84bbbacda5d84b0384a2203aee2929612025-08-20T02:38:35ZengBMCRadiation Oncology1748-717X2024-11-0119111510.1186/s13014-024-02531-5Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurationsYuchao Miao0Jiwei Li1Ruigang Ge2Chuanbin Xie3Yaoying Liu4Gaolong Zhang5Mingchang Miao6Shouping Xu7National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeACCURAY, China National Nuclear CorporationDepartment of Radiation Oncology, The First Medical Center of the People’s Liberation Army General HospitalDepartment of Radiation Oncology, The First Medical Center of the People’s Liberation Army General HospitalSchool of Physics, Beihang UniversitySchool of Physics, Beihang UniversityDepartment of Radiation Oncology, the Fourth Hospital of Hebei Medical UniversityNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeAbstract Background Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients. This study seeks to develop a more versatile model incorporating variable beam configurations of CK and considering the patient’s anatomy. Methods This study proposed that the AB (anatomy and beam) model be compared with the control Mask (only anatomy) model. These models are based on a 3D U-Net network to investigate the impact of CK beam encoding information on dose prediction. The study collected 86 lung cancer patients who received CK′s built-in MC algorithm plans using different beam configurations for training/validation (66 cases) and testing (20 cases). We compared the gamma passing rate, dose difference maps, and relevant dose-volume metrics to evaluate the model’s performance. In addition, the Dice similarity coefficients (DSCs) were calculated to assess the spatial correspondence of isodose volumes. Results The AB model demonstrated superior performance compared to the Mask model, particularly in the trajectory dose of the beam. The DSCs of the AB model were 20–40% higher than that of the Mask model in some dose regions. We achieved approximately 99% for the PTV and generally more than 95% for the organs at risk (OARs) referred to the clinical planning dose in the gamma passing rates (3 mm/3%). Relative to the Mask model, the AB model exhibited more than 90% improvement in small voxels (p < 0.001). The AB model matched well with the clinical plan’s dose-volume histograms, and the average dose error for all organs was 1.65 ± 0.69%. Conclusions Our proposed new model signifies a crucial advancement in predicting CK 3D dose distributions for clinical applications. It enables planners to rapidly and precisely predict MC doses for lung cancer based on patient-specific beam configurations and optimize the CK treatment process.https://doi.org/10.1186/s13014-024-02531-5Automatic planningDeep learningDose predictionMonte CarloCyberKnife |
| spellingShingle | Yuchao Miao Jiwei Li Ruigang Ge Chuanbin Xie Yaoying Liu Gaolong Zhang Mingchang Miao Shouping Xu Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations Radiation Oncology Automatic planning Deep learning Dose prediction Monte Carlo CyberKnife |
| title | Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations |
| title_full | Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations |
| title_fullStr | Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations |
| title_full_unstemmed | Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations |
| title_short | Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations |
| title_sort | dose prediction of cyberknife monte carlo plan for lung cancer patients based on deep learning robust learning of variable beam configurations |
| topic | Automatic planning Deep learning Dose prediction Monte Carlo CyberKnife |
| url | https://doi.org/10.1186/s13014-024-02531-5 |
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