{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:22:50Z","timestamp":1787026970018,"version":"build-2736575974"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>While artificial intelligence has shown great promise in organs-at-risk (OARs) auto segmentation for head and neck cancer (HNC) radiotherapy, to reach the level of clinical acceptance of this technology in real-world routine practice is still a challenge. The purpose of this study was to validate a U-net-based full convolutional neural network (CNN) for the automatic delineation of OARs of HNC, focusing on clinical implementation and evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In the first phase, the CNN was trained on 364 clinical HNC patients\u2019 CT images with annotated contouring from routine clinical cases by different oncologists. The automated delineation accuracy was quantified using the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD). To assess efficiency, the time required to edit the auto-contours to a clinically acceptable standard was evaluated by a questionnaire. For subjective evaluation, expert oncologists (more than 10 years\u2019 experience) were randomly presented with automated delineations or manual contours of 15 OARs for 30 patient cases. In the second phase, the network was retrained with an additional 300 patients, which were generated by pre-trained CNN and edited by oncologists until to meet clinical acceptance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Based on DSC, the CNN performed best for the spinal cord, brainstem, temporal lobe, eyes, optic nerve, parotid glands and larynx (DSC &amp;gt;0.7). Higher conformity for the OARs delineation was achieved by retraining our architecture, largest DSC improvement on oral cavity (0.53 to 0.93). Compared with the manual delineation time, after using auto-contouring, this duration was significantly shortened from hours to minutes. In the subjective evaluation, two observes showed an apparent inclination on automatic OARs contouring, even for relatively low DSC values. Most of the automated OARs segmentation can reach the clinical acceptance level compared to manual delineations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>After retraining, the CNN developed for OARs automated delineation in HNC was proved to be more robust, efficiency and consistency in clinical practice. Deep learning-based auto-segmentation shows great potential to alleviate the labor-intensive contouring of OAR for radiotherapy treatment planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2021.638197","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T11:07:23Z","timestamp":1620212843000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":50,"title":["A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases"],"prefix":"10.3389","volume":"11","author":[{"given":"Yang","family":"Zhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanju","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingtao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiazhou","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weigang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/s1470-2045(10)70290-4","article-title":"Parotid-Sparing Intensity Modulated Versus Conventional Radiotherapy in Head and Neck Cancer (PARSPORT): A Phase 3 Multicentre Randomised Controlled Trial","volume":"12","author":"N","year":"2011","journal-title":"Lancet Oncol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.09.062","article-title":"Emphasizing Conformal Avoidance Versus Target Definition for IMRT Planning in Head-and-Neck Cancer","volume":"77","author":"Harari","year":"2010","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B3","doi-asserted-by":"publisher","first-page":"799","DOI":"10.3109\/0284186x.2016.1173723","article-title":"Use of Auto-Segmentation in the Delineation of Target Volumes and Organs At Risk in Head and Neck","volume":"55","author":"Lim","year":"2016","journal-title":"Acta Oncol (Stockholm Sweden)"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.07.009","article-title":"Clinical Validation of Atlas-Based Auto-Segmentation of Multiple Target Volumes and Normal Tissue (Swallowing\/Mastication) Structures in the Head and Neck","volume":"81","author":"T","year":"2011","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2014.01.027","article-title":"CT-Based Patient Modeling for Head and Neck Hyperthermia Treatment Planning: Manual Versus Automatic Normal-Tissue-Segmentation","volume":"111","author":"Verhaart","year":"2014","journal-title":"Radiother Oncol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"050902","DOI":"10.1118\/1.4871620","article-title":"Vision 20\/20: Perspectives on Automated Image Segmentation for Radiotherapy","volume":"41","author":"S","year":"2014","journal-title":"Med Phys"},{"key":"B7","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1186\/1748-717x-7-160","article-title":"Systematic Evaluation of Three Different Commercial Software Solutions for Automatic Segmentation for Adaptive Therapy in Head-and-Neck, Prostate and Pleural Cancer","volume":"7","author":"LM","year":"2012","journal-title":"Radiat Oncol (London England)"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12197","article-title":"Evaluation of Segmentation Methods on Head and Neck CT: Auto-Segmentation Challenge 2015","volume":"44","author":"R","year":"2017","journal-title":"Med Phys"},{"key":"B9","doi-asserted-by":"publisher","first-page":"071905","DOI":"10.1118\/1.4810971","article-title":"Tissue Segmentation of Head and Neck CT Images for Treatment Planning: A Multiatlas Approach Combined With Intensity Modeling","volume":"40","author":"Fortunati","year":"2013","journal-title":"Med Phys"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.05.012","article-title":"Multi-Subject Atlas-Based Auto-Segmentation Reduces Interobserver Variation and Improves Dosimetric Parameter Consistency for Organs At Risk in Nasopharyngeal Carcinoma: A Multi-Institution Clinical Study","volume":"115","author":"Tao","year":"2015","journal-title":"Radiother Oncol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/tmi.2018.2856464","article-title":"Can Atlas-Based Auto-Segmentation Ever be Perfect? 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