{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:15:13Z","timestamp":1786976113708,"version":"build-2736575974"},"reference-count":33,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study evaluates deep learning (DL) based dose prediction methods in head and neck cancer (HNC) patients using two types of input contours.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Seventy\u2010five HNC patients undergoing two\u2010step volumetric\u2010modulated arc therapy were included. Dose prediction was performed using the AIVOT prototype (AiRato.Inc, Sendai, Japan), a commercial software with an HD U\u2010net\u2010based dose distribution prediction system. Models were developed for the initial plan (46\u00a0Gy\/23Fr) and boost plan (24\u00a0Gy\/12Fr), trained with 65 cases and tested with 10 cases. The 8\u2010channel model used one target (PTV) and seven organs at risk (OARs), while the 10\u2010channel\u00a0model added two dummy contours (PTV ring and spinal cord PRV). Predicted and deliverable doses, obtained through dose mimicking on another radiation treatment planning system, were evaluated using dose\u2010volume indices for PTV and OARs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For the initial plan, both models achieved approximately 2% prediction accuracy for the target dose and maintained accuracy within 3.2\u00a0Gy for OARs. The 10\u2010channel model outperformed the 8\u2010channel model for certain dose indices. For the boost plan, both models exhibited prediction accuracies of approximately 2% for the target dose and 1\u00a0Gy for OARs. The 10\u2010channel model showed significantly closer predictions to the ground truth for D50% and Dmean. Deliverable plans based on prediction doses showed little significant difference compared to the ground truth, especially for the boost plan.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DL\u2010based dose prediction using the AIVOT prototype software in HNC patients yielded promising results. While additional contours may enhance prediction accuracy, their impact on dose mimicking is relatively small.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.14519","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T13:40:34Z","timestamp":1726580434000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Evaluation of deep learning based dose prediction in head and neck cancer patients using two different types of input contours"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8636-4045","authenticated-orcid":false,"given":"Masahide","family":"Saito","sequence":"first","affiliation":[{"name":"Department of Radiology University of Yamanashi  Yamanashi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5018-3800","authenticated-orcid":false,"given":"Noriyuki","family":"Kadoya","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Tohoku Univ. Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuto","family":"Kimura","sequence":"additional","affiliation":[{"name":"Radiation Oncology Center Ofuna Chuo Hospital  Kamakura Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6540-2906","authenticated-orcid":false,"given":"Hikaru","family":"Nemoto","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Yamanashi  Yamanashi Japan"},{"name":"Department of Radiation Oncology Tohoku Univ. Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryota","family":"Tozuka","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Yamanashi  Yamanashi Japan"},{"name":"Department of Radiation Oncology Tohoku Univ. 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