Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy
Background and purpose: Pelvic radiotherapy for gynecologic cancer inevitably irradiates sensitive areas like iliac bones, lumbar vertebrae, and sacrum. Using 18F-FDG PET/CT as a reference, we developed a deep learning method to detect hematopoietic active bone marrow (ABM) on CT in gynecologic canc...
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Elsevier
2025-07-01
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| Series: | Physics and Imaging in Radiation Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631625001289 |
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| _version_ | 1849239519268700160 |
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| author | Zhe Zhang Xiao Lu Sicheng He Tao Huang Shaobin Wang Mingjun Lu Xiaomin Zhang Zhibo Tan John Moraros Lei Zhang Xin Li Zhan Li Zihao Deng Yimeng Zhang Mengjie Dong Shuihua Wang Yajie Liu |
| author_facet | Zhe Zhang Xiao Lu Sicheng He Tao Huang Shaobin Wang Mingjun Lu Xiaomin Zhang Zhibo Tan John Moraros Lei Zhang Xin Li Zhan Li Zihao Deng Yimeng Zhang Mengjie Dong Shuihua Wang Yajie Liu |
| author_sort | Zhe Zhang |
| collection | DOAJ |
| description | Background and purpose: Pelvic radiotherapy for gynecologic cancer inevitably irradiates sensitive areas like iliac bones, lumbar vertebrae, and sacrum. Using 18F-FDG PET/CT as a reference, we developed a deep learning method to detect hematopoietic active bone marrow (ABM) on CT in gynecologic cancer patients and assess clinical benefits. Materials and methods: We analyzed 319 patients from five institutions retrospectively. ABM was divided into three 18F-FDG PET/CT-defined subregions: active iliac bone marrow (A_IBM), active sacral bone marrow (A_SBM), and active lumbar vertebrae bone marrow (A_LVBM), defined as areas with standardized uptake values exceeding subregional means. Six deep learning models were trained: hybrid nnU-Net, U-Net, V-Net, ResU-Net, nnU-Net, and UNETR. The hybrid nnU-Net approach integrated nnU-Net predictions with anatomical bone structures via Boolean operations, providing a post-processing strategy. The dataset was split into 290 cases for training and 29 for independent testing. Performance was evaluated using Dice similarity coefficients (DSCs) and 95th percentile Hausdorff distance (HD95). Two clinical cases were prospectively evaluated for ABM-sparing radiotherapy with hematologic monitoring. Results: The hybrid nnU-Net achieved the highest DSCs for A_IBM (0.74 ± 0.06), A_LVBM (0.79 ± 0.07), and A_SBM (0.75 ± 0.06), with significant improvements over most models (p < 0.001), except nnU-Net. Despite ResU-Net’s lower HD95 in two subregions, hybrid nnU-Net showed superior accuracy. No grade ≥2 hematologic toxicity occurred in prospective cases. Conclusion: This multi-institutional study confirms that the hybrid nnU-Net accurately segments ABM from CT images, showing potential for ABM-sparing radiotherapy. |
| format | Article |
| id | doaj-art-8a1266f048364df78ce5da3a3b14bcdf |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
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| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-8a1266f048364df78ce5da3a3b14bcdf2025-08-20T04:00:55ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162025-07-013510082310.1016/j.phro.2025.100823Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapyZhe Zhang0Xiao Lu1Sicheng He2Tao Huang3Shaobin Wang4Mingjun Lu5Xiaomin Zhang6Zhibo Tan7John Moraros8Lei Zhang9Xin Li10Zhan Li11Zihao Deng12Yimeng Zhang13Mengjie Dong14Shuihua Wang15Yajie Liu16Department of Radiation Oncology, Peking University Shenzhen Hospital, Hong Kong University of Science and Technology Medical Center, Shenzhen, China; Department of Biosciences and Bioinformatics, Suzhou Municipal Key Lab AI4Health, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou, China; Department of Computer Sciences, University of Liverpool, Liverpool, UKDepartment of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInterventional Diagnostic Centre, Zhongnan Hospital Wuhan University, Wuhan, ChinaDepartment of Biosciences and Bioinformatics, Suzhou Municipal Key Lab AI4Health, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou, China; Department of Computer Sciences, University of Liverpool, Liverpool, UKDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Department of Research and Development, MedMind Technology Co, Ltd., Beijing, ChinaDepartment of Radiology, Zhujiang Hospital Southern Medical University, Guangzhou, ChinaDepartment of Radiation Oncology, Peking University Shenzhen Hospital, Hong Kong University of Science and Technology Medical Center, Shenzhen, ChinaDepartment of Radiation Oncology, Proton and Heavy Ion Center, Heyou Hospital, Heyou International Health System, Foshan, Guangdong, ChinaDepartment of Biosciences and Bioinformatics, Suzhou Municipal Key Lab AI4Health, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou, China; Department of Computer Sciences, University of Liverpool, Liverpool, UKDepartment of Radiation Oncology, Peking University Shenzhen Hospital, Hong Kong University of Science and Technology Medical Center, Shenzhen, ChinaDepartment of Radiation Oncology, Peking University Shenzhen Hospital, Hong Kong University of Science and Technology Medical Center, Shenzhen, ChinaDepartment of Nuclear Medicine, Peking University Shenzhen Hospital, Shenzhen, ChinaCollege of Life Sciences and Oceanography, Shenzhen University, Shenzhen, ChinaDepartment of Research and Development, MedMind Technology Co, Ltd., Beijing, ChinaDepartment of Nuclear Medicine, Peking University Shenzhen Hospital, Shenzhen, China; Corresponding author at: Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, ChinaDepartment of Biosciences and Bioinformatics, Suzhou Municipal Key Lab AI4Health, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou, China; Department of Computer Sciences, University of Liverpool, Liverpool, UK; Corresponding author: Department of Biosciences and Bioinformatics, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, ChinaDepartment of Radiation Oncology, Peking University Shenzhen Hospital, Hong Kong University of Science and Technology Medical Center, Shenzhen, China; Corresponding author: Department of Nuclear Medicine, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.Background and purpose: Pelvic radiotherapy for gynecologic cancer inevitably irradiates sensitive areas like iliac bones, lumbar vertebrae, and sacrum. Using 18F-FDG PET/CT as a reference, we developed a deep learning method to detect hematopoietic active bone marrow (ABM) on CT in gynecologic cancer patients and assess clinical benefits. Materials and methods: We analyzed 319 patients from five institutions retrospectively. ABM was divided into three 18F-FDG PET/CT-defined subregions: active iliac bone marrow (A_IBM), active sacral bone marrow (A_SBM), and active lumbar vertebrae bone marrow (A_LVBM), defined as areas with standardized uptake values exceeding subregional means. Six deep learning models were trained: hybrid nnU-Net, U-Net, V-Net, ResU-Net, nnU-Net, and UNETR. The hybrid nnU-Net approach integrated nnU-Net predictions with anatomical bone structures via Boolean operations, providing a post-processing strategy. The dataset was split into 290 cases for training and 29 for independent testing. Performance was evaluated using Dice similarity coefficients (DSCs) and 95th percentile Hausdorff distance (HD95). Two clinical cases were prospectively evaluated for ABM-sparing radiotherapy with hematologic monitoring. Results: The hybrid nnU-Net achieved the highest DSCs for A_IBM (0.74 ± 0.06), A_LVBM (0.79 ± 0.07), and A_SBM (0.75 ± 0.06), with significant improvements over most models (p < 0.001), except nnU-Net. Despite ResU-Net’s lower HD95 in two subregions, hybrid nnU-Net showed superior accuracy. No grade ≥2 hematologic toxicity occurred in prospective cases. Conclusion: This multi-institutional study confirms that the hybrid nnU-Net accurately segments ABM from CT images, showing potential for ABM-sparing radiotherapy.http://www.sciencedirect.com/science/article/pii/S2405631625001289Active bone marrowGynecologic radiotherapyHybrid deep learning segmentationMulti-institutional studyHematologic toxicity |
| spellingShingle | Zhe Zhang Xiao Lu Sicheng He Tao Huang Shaobin Wang Mingjun Lu Xiaomin Zhang Zhibo Tan John Moraros Lei Zhang Xin Li Zhan Li Zihao Deng Yimeng Zhang Mengjie Dong Shuihua Wang Yajie Liu Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy Physics and Imaging in Radiation Oncology Active bone marrow Gynecologic radiotherapy Hybrid deep learning segmentation Multi-institutional study Hematologic toxicity |
| title | Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy |
| title_full | Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy |
| title_fullStr | Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy |
| title_full_unstemmed | Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy |
| title_short | Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy |
| title_sort | hybrid deep learning enables multi institutional delineation of active bone marrow for gynecologic radiotherapy |
| topic | Active bone marrow Gynecologic radiotherapy Hybrid deep learning segmentation Multi-institutional study Hematologic toxicity |
| url | http://www.sciencedirect.com/science/article/pii/S2405631625001289 |
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