{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T09:58:53Z","timestamp":1790330333282,"version":"4.1.0"},"reference-count":8,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>The purpose of this study was to develop, validate, and clinically implement a standardized automated treatment planning framework using Monaco scripting and to evaluate its dosimetric performance, consistency, and organ-at-risk (OAR) sparing compared with conventional manual planning for head-and-neck cancer (HNC) across a multi-institutional network.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>\n                      A Pareto-based multi-criteria optimization template was iteratively developed, validated on &gt;300 retrospective cases, and implemented across 14 centers. Forty-seven HNC patients were replanned using clinically approved plan (CA), AutoPlan (AP), and AutoPlan iteration (API) approaches and categorized into single-target (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 11), two-target (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 35), and three-target (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 1) cohorts. Plan quality was analyzed in ProKnow using scorecard evaluation, boxplots, histograms, and scatter-plot correlations focusing on target coverage and doses to critical organs including brainstem, spinal cord, and bilateral parotids.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Automated workflows demonstrated superior consistency and OAR sparing compared with CA plans. AP achieved the highest overall scorecard performance, while API consistently produced the most stable dose distributions with lower variability and weaker correlations between PTV_High coverage and OAR doses. CA plans exhibited the widest dose variability and stronger OAR dose dependence. Clinically, the automated scripts achieved &gt;70% automation and have been used in over 850 patients with minimal manual intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Automated Monaco scripting significantly improves plan quality, consistency, and OAR sparing in HNC radiotherapy, supporting routine clinical adoption \u2013 particularly of iterative API workflows \u2013 for standardized multi-center practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmp.jmp_47_26","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:01:55Z","timestamp":1790168515000},"page":"557-568","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Automated Radiotherapy Planning for Head-and-neck Cancer: Multi-institutional Development, Validation, and Clinical Implementation of Monaco Scripting"],"prefix":"10.4103","volume":"51","author":[{"given":"Pichandi","family":"Anchineyan","sequence":"first","affiliation":[{"name":"HCG Hospital, Bengaluru, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuvarajan","family":"Sankaran","sequence":"additional","affiliation":[{"name":"HCG Hospital, Bengaluru, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Mahedran","sequence":"additional","affiliation":[{"name":"HCG Hospital, Vijayawada, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sam Skennet","family":"Das","sequence":"additional","affiliation":[{"name":"HCG Hospital, Bengaluru, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"R2-20260925","doi-asserted-by":"crossref","first-page":"2675","DOI":"10.1118\/1.1600739","article-title":"Fuzzy logic guided inverse treatment planning","volume":"30","author":"Yan","year":"2003","journal-title":"Med Phys"},{"key":"R3-20260925","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.1002\/mp.12232","article-title":"Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy","volume":"44","author":"Fan","year":"2017","journal-title":"Med Phys"},{"key":"R4-20260925","doi-asserted-by":"crossref","first-page":"155002","DOI":"10.1088\/1361-6560\/adf02c","article-title":"Automated radiotherapy treatment planning guided by GPT-4Vision","volume":"70","author":"Liu","year":"2025","journal-title":"Phys Med Biol"},{"key":"R5-20260925","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.meddos.2018.02.005","article-title":"Monaco treatment planning system tools and optimization processes","volume":"43","author":"Clements","year":"2018","journal-title":"Med Dosim"},{"key":"R6-20260925","doi-asserted-by":"crossref","first-page":"11","DOI":"10.2478\/pjmpe-2021-0002","article-title":"Evaluation of dosimetric implications of Pareto and constrained mode of optimization for Monaco TPS generated VMAT plans in post-operated carcinoma of the left breast","volume":"27","author":"Hadi","year":"2021","journal-title":"Pol J Med Phys Eng"},{"key":"R8-20260925","doi-asserted-by":"crossref","first-page":"e314","DOI":"10.1016\/j.ijrobp.2020.07.750","article-title":"Automation: The Future of Radiotherapy","volume":"108","author":"Cokelek","year":"2020","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"R9-20260925","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1002\/acm2.12848","article-title":"A plan template-based automation solution using a commercial treatment planning system","volume":"21","author":"Huang","year":"2020","journal-title":"J Appl Clin Med Phys"},{"key":"R10-20260925","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.meddos.2019.02.006","article-title":"Automatizing a nonscripting TPS for optimizing clinical workflow and reoptimizing IMRT\/VMAT plans","volume":"44","author":"Ayala","year":"2019","journal-title":"Med Dosim"}],"container-title":["Journal of Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jmp.jmp_47_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T09:00:53Z","timestamp":1790326853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jmp.jmp_47_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":8,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.4103\/jmp.jmp_47_26","relation":{},"ISSN":["0971-6203","1998-3913"],"issn-type":[{"value":"0971-6203","type":"print"},{"value":"1998-3913","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"2026-02-26","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication 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