{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:02:44Z","timestamp":1790352164096,"version":"4.1.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 12005315, No.12075329"],"award-info":[{"award-number":["No. 12005315, No.12075329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>To investigate the dosimetric impact on target volumes and organs at risk (OARs) when unmodified auto-segmented OAR contours are directly used in the design of treatment plans. <\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>A total of 127 patients with cervical cancer were collected for retrospective analysis, including 105 patients in the training set and 22 patients in the testing set. The 3D U-net architecture was used for model training and auto-segmentation of nine types of organs at risk. The auto-segmented and manually segmented organ contours were used for treatment plan optimization to obtain the AS-VMAT (automatic segmentations VMAT) plan and the MS-VMAT (manual segmentations VMAT) plan, respectively. Geometric accuracy between the manual and predicted contours were evaluated using the Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), and Hausdorff distance (HD). The dose volume histogram (DVH) and the gamma passing rate were used to identify the dose differences between the AS-VMAT plan and the MS-VMAT plan.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results <\/jats:title><jats:p>Average DSC, MDA and HD<jats:sub>95<\/jats:sub> across all OARs were 0.82\u20130.96, 0.45\u20133.21 mm, and 2.30\u201317.31 mm on the testing set, respectively. The D<jats:sub>99%<\/jats:sub> in the rectum and the Dmean in the spinal cord were 6.04 Gy (P = 0.037) and 0.54 Gy (P = 0.026) higher, respectively, in the AS-VMAT plans than in the MS-VMAT plans. The V<jats:sub>20<\/jats:sub>, V<jats:sub>30<\/jats:sub>, and V<jats:sub>40<\/jats:sub> in the rectum increased by 1.35% (P = 0.027), 1.73% (P = 0.021), and 1.96% (P = 0.008), respectively, whereas the V<jats:sub>10<\/jats:sub> in the spinal cord increased by 1.93% (P = 0.011). The differences in other dosimetry parameters were not statistically significant. The gamma passing rates in the clinical target volume (CTV) were 92.72% and 98.77%, respectively, using the 2%\/2 mm and 3%\/3 mm criteria, which satisfied the clinical requirements.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The dose distributions of target volumes were unaffected when auto-segmented organ contours were used in the design of treatment plans, whereas the impact of automated segmentation on the doses to OARs was complicated. We suggest that the auto-segmented contours of tissues in close proximity to the target volume need to be carefully checked and corrected when necessary.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2022.908903","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T13:16:51Z","timestamp":1654089411000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer"],"prefix":"10.3389","volume":"12","author":[{"given":"Along","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yazhi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,6,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.001","article-title":"Advances in Auto-Segmentation","volume":"29","author":"Cardenas","year":"2019","journal-title":"Semin Radiat Oncol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14898","article-title":"Automatic Clinical Target Volume Delineation for Cervical Cancer in CT Images Using Deep Learning","volume":"48","author":"Shi","year":"2021","journal-title":"Med Phys"},{"key":"B3","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1186\/s12885-020-07595-6","article-title":"CT Based Automatic Clinical Target Volume Delineation Using a Dense-Fully Connected Convolution Network for Cervical Cancer Radiation Therapy","volume":"21","author":"Ju","year":"2021","journal-title":"BMC Cancer"},{"key":"B4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/acm2.13446","article-title":"Deep Learning-Based Classification and Structure Name Standardization for Organ at Risk and Target Delineations in Prostate Cancer Radiotherapy","volume":"22","author":"Jamtheim Gustafsson","year":"2021","journal-title":"J Appl Clin Med Phys"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14467","article-title":"Automatic Contouring System for Cervical Cancer Using Convolutional Neural Networks","volume":"47","author":"Rhee","year":"2020","journal-title":"Med Phys"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12045","article-title":"Segmentation of Organs-at-Risks in Head and Neck CT Images Using Convolutional Neural Networks","volume":"44","author":"Ibragimov","year":"2017","journal-title":"Med Phys"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.01.114","article-title":"Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function","volume":"101","author":"Cardenas","year":"2018","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13466","article-title":"Deep Convolutional Neural Network for Segmentation of Thoracic Organs-at-Risk Using Cropped 3D Images","volume":"46","author":"Feng","year":"2019","journal-title":"Med Phys"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.11.012","article-title":"Clinical Evaluation of Atlas and Deep Learning Based Automatic Contouring for Lung Cancer","volume":"126","author":"Lustberg","year":"2018","journal-title":"Radiother. 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