{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:04:28Z","timestamp":1784217868470,"version":"3.55.0"},"reference-count":39,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R37CA214639"],"award-info":[{"award-number":["R37CA214639"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA227289"],"award-info":[{"award-number":["R01CA227289"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA254377"],"award-info":[{"award-number":["R01CA254377"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R37CA214639"],"award-info":[{"award-number":["R37CA214639"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Whole\u2010brain radiotherapy (WBRT) is a common treatment due to its simplicity and effectiveness. While automated Field\u2010in\u2010Field (Auto\u2010FiF) functions assist WBRT planning in modern treatment planning systems, it still requires manual approaches for optimal plan generation including patient\u2010specific hyperparameters definition and plan refinement based on quality feedback.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study introduces an automated WBRT planning pipeline that integrates a deep learning (DL) Hyperparameter Prediction model for patient\u2010specific parameter generation and a large\u2010language model (LLM)\u2010based conversational interface for interactive plan\u00a0refinement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The Hyperparameter Prediction module was trained on 55 WBRT cases using geometric features of clinical target volume (CTV) and organs at risk (OARs) to determine optimal Auto\u2010FiF settings in RayStation treatment planning system. Plans were generated under predicted hyperparameters. For cases in which the generated plan was suboptimal, quality feedback via voice input was captured by a Conversation module, transcribed using Whisper, and interpreted by GPT\u20104o to adjust planning settings. Plan quality was evaluated in 15 independent cases using clinical metrics and expert review, and model explainability was supported through analysis of feature\u00a0importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fourteen of 15 DL\u2010generated plans were clinically acceptable. Normalized to identical CTV D95% as the clinical plans, the DL\u2010generated and clinical plans showed no statistically significant differences in doses to the eyes, lenses, or CTV dose metrics D1% and D99%. The DL\u2010based planning required under 1 min of computation and achieved total workflow execution in approximately 7 min with a single mouse click, compared to 15 min for manual planning. In cases requiring adjustment, the Conversational module successfully improved dose conformity and hotspot\u00a0reduction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed system improves planning efficiency while maintaining clinically acceptable plan quality. It demonstrates the feasibility of combining DL\u2010based hyperparameter prediction with LLM interaction for streamlined, high\u2010quality WBRT\u00a0planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70689","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:34:36Z","timestamp":1783524876000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human\u2010like AI\u2010based auto\u2010field\u2010in\u2010field whole\u2010brain radiotherapy treatment planning with conversation large language model feedback"],"prefix":"10.1002","volume":"27","author":[{"given":"Adnan","family":"Jafar","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5129-1580","authenticated-orcid":false,"given":"An","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gavin","family":"Atkins","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3480-2177","authenticated-orcid":false,"given":"Xiaoyu","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Pennsylvania Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"WenPY McLarenBP LoefflerJS.Treatment of Metastatic Cancer.Lippincott Williams and Wilkins.2001:2655\u20102670."},{"key":"e_1_2_11_3_1","doi-asserted-by":"crossref","unstructured":"McTyreE EmoryT ScottJ ChinnaiyanP.Whole brain radiotherapy for brain metastasis.Surg Neurol Int.2013;4(Suppl 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