Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy
Abstract Background Accurate surface dose calculation is crucial in superficial low‐energy electron beam radiotherapy owing to shallow treatment depths and the risk of skin toxicity. Traditional Monte Carlo (MC) simulations are precise but computationally expensive and time‐consuming. Methods This s...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Precision Radiation Oncology |
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| Online Access: | https://doi.org/10.1002/pro6.70015 |
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| _version_ | 1850107006699962368 |
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| author | Jialin Huang Zhitao Dai Shuai Hu Yuanchun Ye Yuling Chen Ming Li Tianye Niu Jinfen Zheng Yongsheng Huang Yuanjie Bi |
| author_facet | Jialin Huang Zhitao Dai Shuai Hu Yuanchun Ye Yuling Chen Ming Li Tianye Niu Jinfen Zheng Yongsheng Huang Yuanjie Bi |
| author_sort | Jialin Huang |
| collection | DOAJ |
| description | Abstract Background Accurate surface dose calculation is crucial in superficial low‐energy electron beam radiotherapy owing to shallow treatment depths and the risk of skin toxicity. Traditional Monte Carlo (MC) simulations are precise but computationally expensive and time‐consuming. Methods This study combined MC simulations with deep learning to improve both accuracy and speed. DOSXYZnrc was used to simulate low‐energy electron beams for six body sites, generating computed tomography phantoms and corresponding dose distributions. A cascaded 3D U‐Net (C3D) model was trained on these datasets to predict dose distributions rapidly. Results The C3D model demonstrated significant improvements over traditional 3D U‐Net models, achieving a minimum Gamma pass rate of 92.09% and a minimum dose difference pass rate of 93.58%. The model completed dose predictions in just 0.42 seconds, making predictions approximately 140,000 times faster than MC simulations. In the evaluation of dose distributions across six anatomical regions, C3D consistently outperformed other deep learning models (3D U‐Net, Deep Convolutional Neural Network, and HD U‐Net) in both accuracy and robustness. Conclusion The integration of deep learning with MC simulations significantly enhances the efficiency of surface dose calculations in superficial electron beam radiotherapy. The C3D model provides rapid and accurate dose predictions, facilitating efficient treatment planning while maintaining high accuracy. |
| format | Article |
| id | doaj-art-340c3624102d4ebdbb28a8de7ff68c29 |
| institution | OA Journals |
| issn | 2398-7324 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Precision Radiation Oncology |
| spelling | doaj-art-340c3624102d4ebdbb28a8de7ff68c292025-08-20T02:38:41ZengWileyPrecision Radiation Oncology2398-73242025-06-019210811910.1002/pro6.70015Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapyJialin Huang0Zhitao Dai1Shuai Hu2Yuanchun Ye3Yuling Chen4Ming Li5Tianye Niu6Jinfen Zheng7Yongsheng Huang8Yuanjie Bi9School of ScienceShenzhen Campus of Sun Yat‐sen UniversityShenzhenChinaDepartment of Radiation OncologyCancer Hospital Chinese Academy of Medical SciencesShenzhenGuangdongChinaSchool of ScienceShenzhen Campus of Sun Yat‐sen UniversityShenzhenChinaSchool of ScienceShenzhen Campus of Sun Yat‐sen UniversityShenzhenChinaDepartment of Rheumatology and Immunology, The Seventh AffiliatedHospital Sun Yat‐sen UniversityShenzhen GuangdongChinaIncubation Center of Guangdong University of TechnologyShenzhenGuangdongChinaShenzhen Bay LaboratoryShenzhenChinaDermatology, Center for Chronic Disease Prevention of ShenzhenShenzhenChinaSchool of ScienceShenzhen Campus of Sun Yat‐sen UniversityShenzhenChinaSchool of ScienceShenzhen Campus of Sun Yat‐sen UniversityShenzhenChinaAbstract Background Accurate surface dose calculation is crucial in superficial low‐energy electron beam radiotherapy owing to shallow treatment depths and the risk of skin toxicity. Traditional Monte Carlo (MC) simulations are precise but computationally expensive and time‐consuming. Methods This study combined MC simulations with deep learning to improve both accuracy and speed. DOSXYZnrc was used to simulate low‐energy electron beams for six body sites, generating computed tomography phantoms and corresponding dose distributions. A cascaded 3D U‐Net (C3D) model was trained on these datasets to predict dose distributions rapidly. Results The C3D model demonstrated significant improvements over traditional 3D U‐Net models, achieving a minimum Gamma pass rate of 92.09% and a minimum dose difference pass rate of 93.58%. The model completed dose predictions in just 0.42 seconds, making predictions approximately 140,000 times faster than MC simulations. In the evaluation of dose distributions across six anatomical regions, C3D consistently outperformed other deep learning models (3D U‐Net, Deep Convolutional Neural Network, and HD U‐Net) in both accuracy and robustness. Conclusion The integration of deep learning with MC simulations significantly enhances the efficiency of surface dose calculations in superficial electron beam radiotherapy. The C3D model provides rapid and accurate dose predictions, facilitating efficient treatment planning while maintaining high accuracy.https://doi.org/10.1002/pro6.70015Dose predictionDeep learningMonte Carlo simulationLow‐energy electron beamSuperficial treatmentRadiotherapy |
| spellingShingle | Jialin Huang Zhitao Dai Shuai Hu Yuanchun Ye Yuling Chen Ming Li Tianye Niu Jinfen Zheng Yongsheng Huang Yuanjie Bi Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy Precision Radiation Oncology Dose prediction Deep learning Monte Carlo simulation Low‐energy electron beam Superficial treatment Radiotherapy |
| title | Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy |
| title_full | Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy |
| title_fullStr | Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy |
| title_full_unstemmed | Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy |
| title_short | Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy |
| title_sort | deep learning based dose prediction for low energy electron beam superficial radiotherapy |
| topic | Dose prediction Deep learning Monte Carlo simulation Low‐energy electron beam Superficial treatment Radiotherapy |
| url | https://doi.org/10.1002/pro6.70015 |
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