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This study proposes a deep learning (DL) end-to-end (E2E) auto-planning framework and validate its practicality and feasibility for clinical implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A total of 458 cervical cancer VMAT plans were enrolled and split into training, validation, and test cohorts. An E2E auto-planning framework with a two-stage cascaded DL network was developed: Stage 1 predicted coarse dose from CT and structure masks, and Stage 2 refined it using four beam-band priors and a composite loss. Dose-volume histogram (DVH) endpoints from refined predicted dose were converted into Monaco objectives via a scripting module for iterative optimization. Performance was evaluated with Dose, DVH, and snDVH scores, ablations, and comparisons with manual plans in terms of quality, clinical evaluation and deliverability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The proposed DL method achieved the best performance, with Dose score, DVH score and snDVH score of 2.114\u2009\u00b1\u20090.218\u00a0Gy, 1.194\u2009\u00b1\u20090.295\u00a0Gy and 2.027\u2009\u00b1\u20090.586, respectively. Compared with manual plans, E2E auto-plans preserved target volume coverage while reducing all DVH metrics for bladder, rectum, small intestine, and spinal cord by 2% \u2212\u200935% (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). The gamma passing rate of E2E auto-plans was higher than manual plans in the 3%\/3\u00a0mm gamma criterion (98.1% vs. 97.9%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed auto-planning framework demonstrated a high level of automation and clinical applicability, offering a reliable and promising tool to support radiotherapy workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13014-026-02842-9","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T10:26:05Z","timestamp":1776162365000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep learning-driven automated treatment planning framework for cervical cancer patients treated with volumetric modulated arc therapy"],"prefix":"10.1186","volume":"21","author":[{"given":"Boda","family":"Ning","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuyan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenguo","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingfa","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaining","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanling","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"key":"2842_CR1","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.radonc.2010.01.011","volume":"95","author":"A Bertelsen","year":"2010","unstructured":"Bertelsen A, Hansen CR, Johansen J, Brink C. 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The study was conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived by the Ethics Committee\/IRB due to the retrospective nature of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"75"}}