{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:59:19Z","timestamp":1791381559673,"version":"4.3.3"},"reference-count":24,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271177"],"award-info":[{"award-number":["62271177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Brachytherapy planning places radioactive sources around the tumor target to ensure that the tumor is covered by the prescribed dose while maximizing dose sparing for normal organs. In brachytherapy, the mainstream inverse planning methods are Hybrid Inverse Planning Optimization and Inverse Planning Simulated Annealing. Most existing algorithms use a single objective function with weighting parameters to balance tumor and normal tissue importance, but choosing these weights is often subjective and difficult, which can result in biased or suboptimal plans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To propose a seed implantation position optimization algorithm in prostate low\u2010dose\u2010rate brachytherapy (LDRBT) to improve dose distribution and meet clinical dosimetric criteria.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multi\u2010objective non\u2010dominated genetic algorithm was proposed to optimize seed implantation positions. The optimization focused on direct optimization of dose\u2010volume indices and incorporated inverse planning to adjust implantation control parameters. A prostate case with four clinical dose constraints was tested. The proposed algorithm was compared against single\u2010objective optimization to evaluate its effectiveness in handling discrete variables and managing multiple constraints as well.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Single\u2010objective optimization showed limitations, with only 10% of plans meeting some conditions and none satisfying all four constraints. In contrast, the multi\u2010objective optimization approach produced a larger set of Pareto front solutions, with 24.8% of plans meeting some conditions and six plans fully satisfying all constraints. Although the maximum urethral dose was higher in the multi\u2010objective optimization method, it successfully balanced all constraints.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Multi\u2010objective optimization offers significant advantages over single\u2010objective methods in prostate LDRBT, providing a more comprehensive set of feasible solutions that balance conflicting goals. It shows better performance in achieving plans that meet all clinical constraints.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18030","type":"journal-article","created":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T04:59:37Z","timestamp":1754715577000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi\u2010objective optimization method for seed distribution in prostate cancer low\u2010dose\u2010rate brachytherapy"],"prefix":"10.1002","volume":"52","author":[{"given":"Binbing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou Zhejiang China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health Ministry of Industry and Information Technology of P. R. China  Hangzhou Zhejiang China"},{"name":"Zhejiang Cancer Hospital  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou Zhejiang China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health Ministry of Industry and Information Technology of P. R. China  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Hospital  Beijing China"},{"name":"National Center of Gerontology  Beijing China"},{"name":"Institute of Geriatric Medicine Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ze","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou Zhejiang China"},{"name":"Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health Ministry of Industry and Information Technology of P. R. 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