{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T09:40:43Z","timestamp":1792402843129,"version":"4.4.0"},"reference-count":36,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["KY2030000197"],"award-info":[{"award-number":["KY2030000197"]}],"id":[{"id":"10.13039\/501100012226","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":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Radiation therapy treatment planning is a time\u2010consuming trial\u2010and\u2010error process, and the plan quality is heavily dependent on planners' experiences, resulting in a strong demand for automated planning methods that can rapidly generate uniformly high\u2010quality plans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to develop a hybrid automated deep learning\u2010based plan optimization (HALO) framework, integrating deep learning fluence prediction and optimization, multileaf collimator (MLC) sequencing, and GPU\u2010accelerated Monte Carlo dose computation. HALO was designed to ensure robust plan generation for cervical cancer intensity\u2010modulated radiation therapy (IMRT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed HALO framework incorporated several functional modules. First, a dose\u2010guided Fluence Prediction Network (DG\u2010FPN) was developed. A total of 120 cervical cancer clinical IMRT plans were collected to train the DG\u2010FPN, of which 90 plans were assigned for training, 10 for validation, and 20 for testing. The neural network took patients' computed tomography (CT) anatomy as input and predicted the 3D dose distribution and fluence maps. Next, the predicted fluence maps were further optimized to meet dose\u2010volume constraints (DVCs), and then the refined fluence maps were converted into deliverable segments using an MLC sequencing algorithm. Finally, dose calculation was performed using a GPU\u2010accelerated Monte Carlo engine. The 20 patients in the testing set were used to evaluate the HALO method, and the plan deliverability was validated by patient\u2010specific IMRT Quality Assurance (QA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The DG\u2010FPN achieved superior fluence prediction accuracy compared to previous work, with a median mean absolute error (MAE) of 0.055 and a structural similarity index (SSIM) of 0.94. The automated framework generated high\u2010quality plans and reduced dose to adjacent OARs, with V\n                      <jats:sub>50Gy<\/jats:sub>\n                      decreased from 46.5\u00a0\u00b1\u00a05.0% to 41.5\u00a0\u00b1\u00a04.8% (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.008)for bladder, V\n                      <jats:sub>35Gy<\/jats:sub>\n                      decreased from 31.5\u00a0\u00b1\u00a08.2% to 29.2\u00a0\u00b1\u00a07.5% (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.024) for small intestine, while preserving PTV homogeneity and conformity. Importantly, the entire treatment planning time was within 3 minutes, with fluence optimization time decreased by an average of 82.6% after using deep learning prediction. The mean gamma passing rate under the 2%\/2 mm criterion for the patient\u2010specific IMRT QA achieved 97.37\u00a0\u00b1\u00a01.01%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrated the clinical feasibility of the proposed HALO framework for cervical cancer radiotherapy in producing high\u2010quality and deliverable IMRT plans. The proposed automated paradigm can serve as a stand\u2010alone platform for treatment planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70806","type":"journal-article","created":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T04:21:57Z","timestamp":1790396517000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid deep learning framework for automated treatment planning in cervical cancer radiotherapy"],"prefix":"10.1002","volume":"27","author":[{"given":"Haiyan","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Engineering and Applied Physics University of Science and Technology of China  Hefei Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengtai","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Engineering and Applied Physics University of Science and Technology of China  Hefei Anhui 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