{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T14:02:16Z","timestamp":1789740136399,"version":"4.0.1"},"reference-count":27,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T00:00:00Z","timestamp":1785024000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T00:00:00Z","timestamp":1785024000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Volumetric\u2010modulated arc therapy (VMAT) represents a standard of care for head and neck cancer (HNC). However, treatment plan quality depends heavily on the planner's expertise, thereby challenging the standardization of treatment quality. While the clinical application of artificial intelligence (AI) in radiotherapy planning is rapidly increasing, the robustness of these models\u2014for both typical and irregular cases\u2014remains insufficiently understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate the clinical utility and generalizability of an artificial intelligence (AI)\u2010assisted volumetric\u2010modulated arc therapy (VMAT) planning workflow for head and neck cancer (HNC). This study focused on the robustness of AI\u2010assisted planning using dose distribution prediction for various cases including complex anatomical variations, such as bulky tumors and irregular tumor ruptures, and explored the feasibility of quality standardization through a \u201chuman\u2010in\u2010the\u2010loop\u201d intervention approach via re\u2010optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and Materials<\/jats:title>\n                    <jats:p>Twelve VMAT plans from 11 HNC patients, including complex anatomical scenarios with bulky masses and post\u2010rupture anatomical changes, were analyzed. Clinical plans developed by an experienced planner were compared with AI\u2010assisted plans generated using RatoGuide (AiRato Inc.). The AI\u2010assisted planning workflow utilized deep learning\u2010based dose distribution prediction to generate dose\u2010derived ring contours for optimization. A human\u2010in\u2010the\u2010loop intervention was implemented, wherein planners performed manual fine\u2010tuning if initial AI\u2010assisted plans violated the institutional dose constraint of Dmax\u00a0&lt;115%. Dosimetric metrics for the planning target volume (PTV) and organs at risk (OARs), along with Paddick conformity index and monitor units (MU), were evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI\u2010assisted plans achieved PTV coverage comparable to clinical plans while improving OAR sparing, particularly for the spinal cord and brainstem. The mean spinal cord PRV Dmax was reduced from 27.50\u00a0Gy in clinical plans to 23.97\u00a0Gy in AI\u2010assisted plans. Even in cases with bulky or ruptured tumors, AI\u2010assisted planning maintained high conformity and the overall mean Paddick conformity index was comparable to clinical plans. Although initial AI plans exhibited hotspots (Dmax\u00a0&gt;\u00a0115%) in three cases near the body surface, manual fine\u2010tuning of optimization successfully suppressed these hotspots, meeting clinical criteria. AI\u2010assisted planning maintained comparable mean MU values, and reduced the total planning time from approximately 2\u00a0h to 30\u00a0min.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI\u2010assisted planning workflow demonstrated the potential to produce high\u2010quality VMAT plans, even for complex HNC cases. By integrating physicist human\u2010in\u2010the\u2010loop intervention with AI efficiency, this approach could facilitate the standardization of radiotherapy treatment planning quality while supporting patient safety.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70715","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T03:12:30Z","timestamp":1785121950000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI\u2010assisted VMAT planning incorporating deep learning\u2010based dose prediction for head and neck cancer: feasibility of quality standardization and human intervention for irregular cases"],"prefix":"10.1002","volume":"27","author":[{"given":"Seiji","family":"Tomori","sequence":"first","affiliation":[{"name":"Department of Radiology National Hospital Organization Sendai Medical Center  Sendai Miyagi Japan"},{"name":"Department of Therapeutic Radiology, Course of Radiological Technology, Health Sciences Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kakutaro","family":"Narazaki","sequence":"additional","affiliation":[{"name":"Department of Radiology National Hospital Organization Sendai Medical Center  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shun","family":"Tasaka","sequence":"additional","affiliation":[{"name":"Department of Radiology National Hospital Organization Sendai Medical Center  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,26]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.04.011"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.12.033"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470\u20102045(10)70290\u20104"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2011.11.012"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4761864"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4757927"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13526"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13614"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41571\u2010020\u20100417\u20108"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13271"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrr\/rrz051"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ab039b"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010021\u201001864\u20109"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010025\u201002634\u20107"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrr\/rrad058"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70291"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13583"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14519"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.78388"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20220934"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2013.10.010"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/jicru\/ndq002"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14827"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.3171\/jns.2000.93.supplement_3.0219"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/bmt.2012.244"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.carj.2019.08.010"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110345"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70715","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/acm2.70715","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70715","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:06:47Z","timestamp":1789736807000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.70715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,26]]},"references-count":27,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["10.1002\/acm2.70715"],"URL":"https:\/\/doi.org\/10.1002\/acm2.70715","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,26]]},"assertion":[{"value":"2026-02-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70715"}}