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Tytuł:
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Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy.
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Autorzy:
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Chang Y; School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China.
Liang Y; Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China.
Yang B; Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China.
Qiu J; Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China. qj_.
Pei X; School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China.; Technology Development Department, Anhui Wisdom Technology Co., Ltd., Hefei, China.
Xu XG; School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China. .; Department of Radiation Oncology, First Affiliated Hospital of University of Science and Technology of China, Hefei, China. .
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Źródło:
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Radiation oncology (London, England) [Radiat Oncol] 2023 Jan 05; Vol. 18 (1), pp. 3. Date of Electronic Publication: 2023 Jan 05.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: [London] : BioMed Central, 2006-
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MeSH Terms:
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Uterine Cervical Neoplasms*/diagnostic imaging
Uterine Cervical Neoplasms*/radiotherapy
Spiral Cone-Beam Computed Tomography*
Female ; Humans ; Radiotherapy Dosage ; Organs at Risk ; Radiotherapy Planning, Computer-Assisted/methods ; Cone-Beam Computed Tomography/methods ; Image Processing, Computer-Assisted/methods
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References:
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Contributed Indexing:
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Keywords: CBCT; Cervical cancer; Deformable image registration; Dosimetric comparison; sCT generation
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Entry Date(s):
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Date Created: 20230105 Date Completed: 20230109 Latest Revision: 20230111
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Update Code:
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20240105
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PubMed Central ID:
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PMC9817400
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DOI:
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10.1186/s13014-022-02191-3
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PMID:
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36604687
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Objective: Anatomical variations existing in cervical cancer radiotherapy treatment can be monitored by cone-beam computed tomography (CBCT) images. Deformable image registration (DIR) from planning CT (pCT) to CBCT images and synthetic CT (sCT) image generation based on CBCT are two methods for improving the quality of CBCT images. This study aims to compare the accuracy of these two approaches geometrically and dosimetrically in cervical cancer radiotherapy.
Methods: In this study, 40 paired pCT-CBCT images were collected to evaluate the accuracy of DIR and sCT generation. The DIR method was based on a 3D multistage registration network that was trained with 150 paired pCT-CBCT images, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN) with 6000 paired pCT-CBCT slices for training. Then, the doses were recalculated with the CBCT, pCT, deformed pCT (dpCT) and sCT images by a GPU-based Monte Carlo dose code, ArcherQA, to obtain Dose CBCT , Dose pCT , Dose dpCT and Dose sCT . Organs at risk (OARs) included small intestine, rectum, bladder, spinal cord, femoral heads and bone marrow, CBCT and pCT contours were delineated manually, dpCT contours were propagated through deformation vector fields, sCT contours were auto-segmented and corrected manually.
Results: The global gamma pass rate of Dose sCT and Dose dpCT was 99.66% ± 0.34%, while that of Dose CBCT and Dose dpCT was 85.92% ± 7.56% at the 1%/1 mm criterion and a low-dose threshold of 10%. Based on Dose dpCT as uniform dose distribution, there were comparable errors in femoral heads and bone marrow for the dpCT and sCT contours compared with CBCT contours, while sCT contours had lower errors in small intestine, rectum, bladder and spinal cord, especially for those with large volume difference of pCT and CBCT.
Conclusions: For cervical cancer radiotherapy, the DIR method and sCT generation could produce similar precise dose distributions, but sCT contours had higher accuracy when the difference in planning CT and CBCT was large.
(© 2023. The Author(s).)
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