{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:03:21Z","timestamp":1787313801573,"version":"build-2736575974"},"reference-count":29,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"General program of National Natural Science Foundation of China","award":["31971113"],"award-info":[{"award-number":["31971113"]}]},{"name":"Chongqing key research and development program","award":["CSTB2022TIAD-KPX0181"],"award-info":[{"award-number":["CSTB2022TIAD-KPX0181"]}]},{"name":"Chongqing Science and Technology Talent Project","award":["CQYC201905037"],"award-info":[{"award-number":["CQYC201905037"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients\u2019 plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83%\u2009\u00b1\u20090.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95%\u2009\u00b1\u20096.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19\u2009\u00b1\u20090.86%, and the minimum MAE was 0.94\u2009\u00b1\u20090.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.<\/jats:p>","DOI":"10.1177\/15330338241242654","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T01:56:09Z","timestamp":1712541369000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy"],"prefix":"10.1177","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5191-0248","authenticated-orcid":false,"given":"Zhe","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China"},{"name":"Department of Radiation Oncology, Zigong Disease Prevention and Control Center Mental Health Center, Zigong First People's Hospital, Zigong, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mujun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Zigong Disease Prevention and Control Center Mental Health Center, Zigong First People's Hospital, Zigong, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihua","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of the Army Medical University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,4,7]]},"reference":[{"key":"bibr1-15330338241242654","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"bibr2-15330338241242654","doi-asserted-by":"publisher","DOI":"10.1016\/j.canep.2018.08.008"},{"key":"bibr3-15330338241242654","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-021-01885-z"},{"key":"bibr4-15330338241242654","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc2030016"},{"key":"bibr5-15330338241242654","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. 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UNETR: Transformers for 3D Medical Image Segmentation. 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