{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T05:50:53Z","timestamp":1774158653929,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:00:00Z","timestamp":1772928000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"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":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,3]]},"abstract":"Abstract<\/jats:title>\n \n Background and purpose<\/jats:title>\n Manual planning in breast cancer radiotherapy is often time\u2010consuming and operator\u2010dependent, leading to inconsistencies in plan quality. This study validated an automated workflow using overlap volume histograms (OVH) to predict patient\u2010specific dose\u2013volume histogram (DVH) constraints, aiming to enhance cardiopulmonary sparing and planning efficiency.<\/jats:p>\n <\/jats:sec>\n \n Materials and methods<\/jats:title>\n \n A historical database of 322 patients was stratified into four groups: left\/right post\u2010mastectomy radiotherapy (PMRMRT) and left\/right breast\u2010conserving radiotherapy (BCRT). Linear regression models were established to correlate OVH\u2010derived geometric metrics (\n \n L\n x<\/jats:sub>\n <\/jats:italic>\n ) with corresponding DVH\u2010based dose constraints (\n \n D\n x<\/jats:sub>\n <\/jats:italic>\n ). These predictive models were integrated into the Monaco treatment planning system via a custom Python script to provide an improved automated planning workflow. The workflow's performance was prospectively validated on 80 independent testing cases (20 per group). Automated plans were generated using the predicted constraints and compared dosimetrically against clinically approved manual plans.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Significant linear correlations were observed between\n \n L\n x<\/jats:sub>\n <\/jats:italic>\n and\n \n D\n x<\/jats:sub>\n <\/jats:italic>\n for all OARs (\n \n r\n 2<\/jats:sup>\n <\/jats:italic>\n \u00a0=\u00a00.51\u20130.72,\n p<\/jats:italic>\n \u00a0<\u00a00.001). In the PMRMRT testing cohorts, the automated workflow significantly reduced doses to the heart and ipsilateral lung compared to manual planning (\n p<\/jats:italic>\n \u00a0<\u00a00.05). For left\u2010sided PMRMRT, the heart dose was reduced by 15.6% (\n D<\/jats:italic>\n 10<\/jats:sub>\n ), 18.7% (\n D<\/jats:italic>\n 5<\/jats:sub>\n ), and 9.8% (\n D<\/jats:italic>\n mean<\/jats:sub>\n ), while the ipsilateral lung dose decreased by up to 6.3% (\n D<\/jats:italic>\n mean<\/jats:sub>\n ). In BCRT cases, automated plans were not significant improved compared to manual plans. Importantly, all automated plans maintained target volume coverage and dose homogeneity comparable to manual plans (\n p<\/jats:italic>\n \u00a0>\u00a00.05).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The OVH\u2010based framework effectively translated anatomy into achievable objectives, significantly improving heart and lung sparing for complex PMRMRT cases while streamlining clinical workflows.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/acm2.70513","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T17:08:26Z","timestamp":1772989706000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["OVH\u2010guided planning for superior heart and lung sparing in breast cancer radiotherapy"],"prefix":"10.1002","volume":"27","author":[{"given":"Hao","family":"Lei","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Breast cancer center Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology National key clinical specialty construction discipline Hubei Provincial Clinical Research Center for Breast Cancer Wuhan Clinical Research Center for Breast Cancer Wuhan Hubei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhong","family":"Wu","sequence":"additional","affiliation":[{"name":"Breast cancer center Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology National key clinical specialty construction discipline Hubei Provincial Clinical Research Center for Breast Cancer Wuhan Clinical Research Center for Breast Cancer Wuhan Hubei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xudong","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Hubei Cancer Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,8]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(24)00747\u20105"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jncc.2024.01.006"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20180270"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010019\u20101257\u20105"},{"issue":"3","key":"e_1_2_10_6_1","first-page":"521","article-title":"A statistical approach for achievable dose querying in IMRT planning","volume":"13","author":"Simari P","year":"2010","journal-title":"Med Image Comput Comput Assist 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