{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T20:54:19Z","timestamp":1791233659493,"version":"4.1.0"},"reference-count":55,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003447","name":"State Scholarships Foundation (\u0399\u039a\u03a5)","doi-asserted-by":"publisher","award":["MIS-5000432"],"award-info":[{"award-number":["MIS-5000432"]}],"id":[{"id":"10.13039\/501100003447","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.<\/jats:p>","DOI":"10.3390\/cancers14153573","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T12:53:45Z","timestamp":1658494425000},"page":"3573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6414-890X","authenticated-orcid":false,"given":"Vasiliki","family":"Iliadou","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8365-2140","authenticated-orcid":false,"given":"Ioannis","family":"Kakkos","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece"},{"name":"Department of Biomedical Engineering, University of West Attica, 122 43 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pantelis","family":"Karaiskos","sequence":"additional","affiliation":[{"name":"Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2082-7323","authenticated-orcid":false,"given":"Vassilis","family":"Kouloulias","sequence":"additional","affiliation":[{"name":"2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2977-7895","authenticated-orcid":false,"given":"Kalliopi","family":"Platoni","sequence":"additional","affiliation":[{"name":"2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Zygogianni","sequence":"additional","affiliation":[{"name":"1st Department of Radiology, Radiotherapy Unit, ARETAIEION University Hospital, 115 28 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2600-9914","authenticated-orcid":false,"given":"George K.","family":"Matsopoulos","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1016\/S0140-6736(08)60728-X","article-title":"Head and Neck Cancer","volume":"371","author":"Argiris","year":"2008","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jmir.2015.09.012","article-title":"Cone Beam Computed Tomography: The Challenges and Strategies in Its Application for Dose Accumulation","volume":"47","author":"Kong","year":"2016","journal-title":"J. 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