{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T11:42:47Z","timestamp":1792669367177,"version":"4.5.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Strahlenther Onkol"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Radiotherapy (RT) planning for head and neck squamous cell carcinoma (HNSCC) is known to be both challenging and time-consuming. Deep learning (DL)-based auto-segmentation of organs at risk (OARs) may streamline this procedure, although studies have mainly evaluated the geometric accuracy of DL-based contours. Here, we report on a\u00a0prospective study of inverse treatment planning using DL-based OARs in HNSCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective single-center study enrolled 25 patients undergoing definitive or postoperative (chemo-)radiotherapy for HNSCC. Deep learning-based OAR contours were generated using a\u00a0commercially available auto-segmentation software and rated independently by four radiation oncologists. Clinical RT plans were compared with plans that were re-optimized based on DL-based OARs, while the clinical target volumes (CTV) and planning target volumes (PTV) were kept unchanged. Dosimetric parameters for CTV\/PTV and manually delineated OARs were compared using Wilcoxon\u2019s signed-rank test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A\u00a0total of 259 DL-based contours were rated, with mean scores of &gt;\u202f4 (acceptable with corrections) for all structures. For CTV\/PTV coverage, the mean dosimetric differences between clinical and DL-based plans were &lt;\u202f1\u202fGy for all parameters (D95%, D98%, D2%, Dmean). Similarly, differences between doses to \u201ctrue\u201d (manual) OARs were largely negligible, with one non-critical outlier observed for the spinal cord. All DL-based treatment plans were deemed clinically acceptable after manual review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Inverse treatment planning using DL-based OARs in head and neck RT is feasible and results in clinically acceptable plans. Larger studies are required to confirm these results and to move further toward fully automated workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00066-026-02519-y","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:17:08Z","timestamp":1774883828000},"page":"1029-1036","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Inverse treatment planning using deep learning-based organs at risk in radiotherapy for head and neck cancer: a\u00a0prospective planning study"],"prefix":"10.1007","volume":"202","author":[{"given":"Tristan","family":"Bauer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Weinhold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrich","family":"Schratzenstaller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Bunea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Giambattista","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jon","family":"Giambattista","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandros","family":"Papachristofilou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4118-4171","authenticated-orcid":false,"given":"Tobias","family":"Finazzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"2519_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/medsci11020042","volume":"11","author":"A Barsouk","year":"2023","unstructured":"Barsouk\u00a0A, Aluru\u00a0JS, Rawla\u00a0P et\u00a0al (2023) Epidemiology, Risk Factors, and Prevention of Head and Neck Squamous Cell Carcinoma. 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Tristan\u00a0Bauer, Oliver\u00a0Weinhold, Ulrich\u00a0Schratzenstaller, Andrei\u00a0Bunea, Alexandros\u00a0Papachristofilou and Tobias\u00a0Finazzi declare that they have no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Ethics Committee of Northwestern and Central Switzerland (BASEC ID 2020-02405). All research was conducted in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","label":"Ethical standards","group":{"name":"EthicsHeading","label":"Declarations"}}]}}