{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:12:09Z","timestamp":1778778729633,"version":"3.51.4"},"reference-count":14,"publisher":"IOP Publishing","issue":"10","license":[{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2021,5,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Purpose<\/jats:italic>\n                    . Auto-contouring (AC) is rapidly becoming standard practice for OAR contouring. However, in clinical practice, clinicians still need to manually check and correct contours. Anomaly detection systems (ADS) can aid the clinical decision process by suggesting which structures require corrections or not, greatly enhancing the value of AC. The purpose of this work is to develop and evaluate a decision support system for detecting anomalies in the case of parotid gland delineations. Methods. Head and neck parotid gland delineations (1037 right, 1038 left), were retrieved from the Netherlands Cancer Institute (NKI) database. Morphological and image-based features were extracted from each patient\u2019s CT and structure set. An isolation forest model was initially trained on 70% of the data, of which 10% had synthetically generated anomalies and validated on the remaining 30% of clinical data. The ADS was tested on an independent set of 250 patients (Normal: 174, Anomalies: 76) and on a clinical autocontouring software. Results. Applied to the validation set, the ADS system resulted in area under the curve (AUC) values of 0.93 and 0.94 for the parotid left and right respectively. Image features appeared more important than morphological, but using all features resulted marginally in the best model. Applied to the test set the ADS system reached an accuracy level of 0.83 and 0.81 for the parotid left and right respectively. The ADS was particularly sensitive to uniform expansions\/contractions, misplacements, extra\/missing slices and anisotropic over-contouring. Conclusion. Anomaly detection can serve as a powerful contour quality assurance tool, especially for cases of organ misplacement and over-contouring.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/abfbf5","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T19:23:00Z","timestamp":1619551380000},"page":"105017","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["An anomaly detector as a clinical decision support system for parotid gland delineations"],"prefix":"10.1088","volume":"66","author":[{"given":"Pavlos","family":"Papaconstadopoulos","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Casper","family":"Carbaat","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rita","family":"Sim\u00f5es","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Herbert","family":"Beemster","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Folkert","family":"Koetsveld","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Remeijer","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomas","family":"Janssen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam,"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"pmbabfbf5bib1","doi-asserted-by":"publisher","first-page":"5199","DOI":"10.1088\/0031-9155\/60\/13\/5199","type":"journal-article","article-title":"A framework for automated contour quality assurance in radiation therapy including adaptive techniques","volume":"60","author":"Altman","year":"2015","journal-title":"Phys. Med. Biol."},{"key":"pmbabfbf5bib2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-46150-8_13","type":"book","article-title":"Robust anomaly detection in images using adversarial autoencoders","volume":"11906","author":"Beggel","year":"2020"},{"key":"pmbabfbf5bib3","doi-asserted-by":"publisher","first-page":"E703","DOI":"10.1016\/j.ijrobp.2012.02.003","type":"journal-article","article-title":"A system for continual quality improvement of normal tissue delineation for radiation therapy treatment planning","volume":"83","author":"Breunig","year":"2012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabfbf5bib4","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.radonc.2015.07.041","type":"journal-article","article-title":"CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines","volume":"117","author":"Brouwer","year":"2015","journal-title":"Radiother. Oncol."},{"key":"pmbabfbf5bib5","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/1748-717X-7-32","type":"journal-article","article-title":"3D Variation in delineation of head and neck organs at risk","volume":"7","author":"Brouwer","year":"2012","journal-title":"Radiat. Oncol."},{"key":"pmbabfbf5bib6","doi-asserted-by":"publisher","first-page":"P60","DOI":"10.1016\/j.meddos.2019.06.002","type":"journal-article","article-title":"Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: time, geometrical, and dosimetric analysis","volume":"45","author":"Fung","year":"2019","journal-title":"Med. Dosim."},{"key":"pmbabfbf5bib7","doi-asserted-by":"publisher","first-page":"S714","DOI":"10.1016\/j.ijrobp.2013.06.1892","type":"journal-article","article-title":"\u2018Multicenter clinical assessment of DIR atlas-based autocontouring","volume":"87","author":"Gooding","year":"2013","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabfbf5bib8","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1002\/mp.12835","type":"journal-article","article-title":"Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach","volume":"45","author":"Hui","year":"2018","journal-title":"Med. Phys."},{"key":"pmbabfbf5bib9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133363","type":"journal-article","article-title":"Isolation-based anomaly detection","volume":"6","author":"Liu","year":"2012","journal-title":"ACM Trans. Knowl. Discovery Data"},{"key":"pmbabfbf5bib10","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.ijrobp.2010.10.019","type":"journal-article","article-title":"Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer","volume":"82","author":"Nelms","year":"2012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbabfbf5bib11","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","type":"journal-article","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"Van Griethuysen","year":"2017","journal-title":"Cancer Res."},{"key":"pmbabfbf5bib12","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.radonc.2016.09.009","type":"journal-article","article-title":"Uncertainties in volume delineation in radiation oncology: a systematic review and recommendations for future studies","volume":"121","author":"Vinod","year":"2016","journal-title":"Radiother. Oncol."},{"key":"pmbabfbf5bib13","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.radonc.2014.08.028","type":"journal-article","article-title":"Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer","volume":"112","author":"Walker","year":"2014","journal-title":"Radiother. Oncol."},{"key":"pmbabfbf5bib14","doi-asserted-by":"publisher","first-page":"4010","DOI":"10.1002\/mp.13697","type":"journal-article","article-title":"Texture\u2010based, automatic contour validation for online adaptive replanning: a feasibility study on abdominal organs","volume":"46","author":"Zhang","year":"2019","journal-title":"Med. Phys."}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:32:43Z","timestamp":1778776363000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/abfbf5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,14]]},"references-count":14,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5,14]]},"published-print":{"date-parts":[[2021,5,21]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/abfbf5","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,14]]},"assertion":[{"value":"An anomaly detector as a clinical decision support system for parotid gland delineations","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2021 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2020-11-20","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-04-27","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-05-14","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}