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A new x\u2010ray\u2010based adaptive radiotherapy (ART) treatment planning system (TPS) version 2.0 emulator includes novel methods to drive the planning process including the revised intelligent optimization engine algorithm (IOE2). HNC is among the most challenging and complex sites and heavily depends on planner skill and experience to successfully generate a reference plan. Therefore, we evaluate the new TPS performance via conventionally accepted planning strategies with\/without artificial intelligence (AI) and knowledge\u2010based planning (KBP).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our institution has a pre\u2010clinical release of the Varian Ethos2.0 TPS emulator which includes several changes that may affect current planning strategies. Twenty definitive and post\u2010operative HNC patients were retrospectively selected with a two or three\u2010level simultaneous integrated boost (SIB) dosing scheme. Patients were replanned in the emulator using population\u2010based, KBP\u2010guided with\/without human intervention and AI\u2010guided planning goals. These planning strategies were compared both dosimetrically and for plan deliverability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>All strategies generally demonstrated acceptable plan quality with KBP\u2010 and AI\u2010guided goals offering enhanced dosimetric sparing in organs\u2010at\u2010risk (OAR). The average contralateral parotid gland mean dose was 20.0\u00a0\u00b1\u00a06.1\u00a0Gy (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001) for population\u2010based and 15.0\u00a0\u00b1\u00a06.1\u00a0Gy (<jats:italic>p<\/jats:italic>\u00a0=\u00a0n.s.) for KBP\u2010with human intervention versus 15.1\u00a0\u00b1\u00a07.4\u00a0Gy for clinical plans. Target coverage, minimum dose, and plan hotspot were acceptable in all cases. KBP\u2010enabled strategy demonstrated higher modulation and faster optimization time than both population\u2010based and AI\u2010guided strategies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Simply entering population, automatic KBP\u2010enabled or AI\u2010generated planning goals into the new Ethos2.0 TPS produced dosimetrically compliant plans, with AI\u2010guided goals demonstrating the most OAR sparing. Several of these approaches are easy to translate to other treatment sites and will help lower the barrier to entry for x\u2010ray\u2010based online\u2010ART.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14576","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T19:43:53Z","timestamp":1733255033000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Assessing population\u2010based to personalized planning strategies for head and neck adaptive radiotherapy"],"prefix":"10.1002","volume":"26","author":[{"given":"Justin","family":"Visak","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas USA"},{"name":"Medical Artificial Intelligence and Automation Laboratory Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas 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