{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:45:30Z","timestamp":1792071930146,"version":"4.4.0"},"reference-count":20,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"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":["Medical Physics"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Knowledge\u2010based planning (KBP) has improved the quality and efficiency of radiotherapy treatment planning. However, its broader clinical adoption remains limited because effective deployment often requires institution\u2010specific model training and tuning. Publicly available KBP models provide a convenient starting point but may not consistently meet local clinical objectives across institutions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We developed and evaluated the Planning Copilot, a large language model (LLM)\u2010guided plan refinement framework designed to operate as a model\u2010agnostic post\u2010processing layer for KBP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The Planning Copilot is a closed\u2010loop, multi\u2010agent system that iteratively refines KBP\u2010generated plans through structured dosimetric feedback and the selection of clinically validated optimization actions within a treatment planning system. For each case, an initial step\u2010and\u2010shoot IMRT plan was generated with each of three RapidPlan models, including a publicly available model and two institutional models with different optimization constraints. To assess whether the refinement depends on KBP, we additionally evaluated PlanningCopilot starting from a non\u2010KBP fixed objective template applied identically to all cases. The PlanningCopilot was applied without model\u2010specific tuning to 62 retrospective locally advanced NSCLC cases. Clinical goal achievement rates and clinically relevant dose\u2013volume metrics were compared between the initial KBP plans and the refined plans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Across all three KBP models, the PlanningCopilot substantially improved plan quality. Clinical goal achievement increased from 79% to 98% for the UCSD model, from 73% to 97% for Institutional T1, and from 69% to 98% for Institutional T2. Starting from the non\u2010KBP fixed template, the achievement rate increased from 68% to 97%, comparable to the KBP initializations. Significant reductions were observed in key lung dose metrics, including lung Dmean across all models and lung V20 in the Institutional T1 model and template initializations, while target coverage and doses to critical structures were maintained. Notably, the KBP model that prioritized OAR sparing, which exhibited the lowest initial pass rate, showed the highest rescue rate after refinement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>An LLM\u2010guided refinement layer can improve the success rate and portability of KBP across heterogeneous models without retraining the underlying KBP system. This approach provides a practical strategy to enhance the reliability of KBP and supports the use of off\u2010the\u2010shelf models through automated, model\u2010agnostic post\u2010processing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70657","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T07:48:42Z","timestamp":1788335322000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving portability of knowledge\u2010based planning using an LLM\u2010driven plan refinement framework in lung radiotherapy"],"prefix":"10.1002","volume":"53","author":[{"given":"Zipai","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Samstein","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth E","family":"Rosenzweig","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Chao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Icahn School of Medicine at Mount Sinai  New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,2]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3857\/roj.2018.00122"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/51\/13\/R21"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2016.04.005"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2011.11.012"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.12.034"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2018.00057"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010019\u20101403\u20100"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70304"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010023\u201002448\u20108"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010023\u201006291\u20102"},{"key":"e_1_2_8_12_1","article-title":"Integrating large language models for enhanced predictive analytics in healthcare","author":"Wang Y","year":"2026","journal-title":"npj Digit Med"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s44387-025-00015-9"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2023.11.062"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.4630"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2023.101400"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/adbff1"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2406.15609"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.70665"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.20.00093"},{"key":"e_1_2_8_21_1","doi-asserted-by":"crossref","unstructured":"WangZ GuoH LeiY. et al.Improving portability of knowledge\u2010based planning using an LLM\u2010driven plan refinement framework. 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