{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:46:49Z","timestamp":1792082809604,"version":"4.4.0"},"reference-count":30,"publisher":"IOP Publishing","issue":"9","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["F31CA243504"],"award-info":[{"award-number":["F31CA243504"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,5,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).\n                    <jats:italic>Approach.<\/jats:italic>\n                    The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN). DPN predicts organs at risk (OAR)\n                    <jats:italic>D<\/jats:italic>\n                    <jats:sub>\n                      2\n                      <jats:italic>cc<\/jats:italic>\n                    <\/jats:sub>\n                    and CTV\n                    <jats:italic>D<\/jats:italic>\n                    <jats:sub>90%<\/jats:sub>\n                    of the current fraction from the patient\u2019s current anatomy and prescription dose of HDRBT. PPN outputs the probability of a given plan being acceptable to the physician based on the patients anatomy and the total dose combining HDRBT and external beam radiotherapy sessions. Training of the networks was achieved by first training them separately for a good initialization, and then jointly via an adversarial process. We collected approved treatment plans of 248 treatment fractions from 63 patients. Among them, 216 plans from 54 patients were employed in a four-fold cross validation study, and the remaining 32 plans from other 9 patients were saved for independent testing.\n                    <jats:italic>Main results.<\/jats:italic>\n                    DPN predicted equivalent dose of 2 Gy for bladder, rectum, sigmoid\n                    <jats:italic>D<\/jats:italic>\n                    <jats:sub>\n                      2\n                      <jats:italic>cc<\/jats:italic>\n                    <\/jats:sub>\n                    and CTV\n                    <jats:italic>D<\/jats:italic>\n                    <jats:sub>90%<\/jats:sub>\n                    with a relative error of 11.51% \u00b1 6.92%, 8.23% \u00b1 5.75%, 7.12% \u00b1 6.00%, and 10.16% \u00b1 10.42%, respectively. In a task that differentiates clinically approved plans and disapproved plans generated by perturbing doses in ground truth approved plans by 20%, PPN achieved accuracy, sensitivity, specificity, and area under the curve 0.70, 0.74, 0.65, and 0.74.\n                    <jats:italic>Significance.<\/jats:italic>\n                    We demonstrated the feasibility of developing a novel deep-learning framework that predicts a probability of plan approval for HDRBT of cervical cancer, which is an essential component in automatic treatment planning.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad3880","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T18:43:17Z","timestamp":1711564997000},"page":"095010","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning"],"prefix":"10.1088","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6998-2087","authenticated-orcid":true,"given":"Yin","family":"Gao","sequence":"first","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yesenia","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chika","family":"Nwachukwu","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Albuquerque","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6159-2909","authenticated-orcid":true,"given":"Xun","family":"Jia","sequence":"additional","affiliation":[{"name":"Johns Hopkins University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"pmbad3880bib1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1002\/mp.13262","type":"journal-article","article-title":"A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning","volume":"46","author":"Chen","year":"2019","journal-title":"Med. Phys."},{"key":"pmbad3880bib2","doi-asserted-by":"publisher","first-page":"e534","DOI":"10.1016\/j.ijrobp.2018.07.1497","type":"journal-article","article-title":"The analytical hierarchy process (ahp) to score plan quality of intact prostate treatment plans","volume":"102","author":"Delgado","year":"2018","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbad3880bib3","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac6d9e","type":"journal-article","article-title":"Modeling physician\u2019s preference in treatment plan approval of stereotactic body radiation therapy of prostate cancer","volume":"67","author":"Gao","year":"2022","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib4","doi-asserted-by":"publisher","first-page":"2760","DOI":"10.1002\/mp.13526","type":"journal-article","article-title":"Knowledge-based planning for intensity-modulated radiation therapy: a review of data-driven approaches","volume":"46","author":"Ge","year":"2019","journal-title":"Med. Phys."},{"key":"pmbad3880bib5","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.ijrobp.2010.12.029","type":"journal-article","article-title":"Dose effect relationship for late side effects of the rectum and urinary bladder in magnetic resonance image-guided adaptive cervix cancer brachytherapy","volume":"82","author":"Georg","year":"2012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbad3880bib6","volume":"Vol 6","author":"Hall","year":"2006","type":"book"},{"key":"pmbad3880bib7","article-title":"Adversarial machine learning at scale","author":"Kurakin","year":"2016","type":"preprint"},{"key":"pmbad3880bib8","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.radonc.2007.10.018","type":"journal-article","article-title":"A new dose-volume-based plan quality index for imrt plan comparison","volume":"85","author":"Leung","year":"2007","journal-title":"Radiother. Oncol."},{"key":"pmbad3880bib9","doi-asserted-by":"publisher","first-page":"8725","DOI":"10.1088\/0031-9155\/58\/24\/8725","type":"journal-article","article-title":"Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan dvhs","volume":"58","author":"Li","year":"2013","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib10","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1088\/0031-9155\/52\/3\/022","type":"journal-article","article-title":"Reduced-order parameter optimization for simplifying prostate imrt planning","volume":"52","author":"Lu","year":"2007","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib11","doi-asserted-by":"publisher","first-page":"3257","DOI":"10.1109\/TMI.2019.2927182","type":"journal-article","article-title":"Deep adversarial training for multi-organ nuclei segmentation in histopathology images","volume":"39","author":"Mahmood","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbad3880bib12","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.brachy.2014.01.004","type":"journal-article","article-title":"Implant time and process efficiency for ct-guided high-dose-rate brachytherapy for cervical cancer","volume":"13","author":"Mayadev","year":"2014","journal-title":"Brachytherapy"},{"key":"pmbad3880bib13","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.radonc.2016.06.006","type":"journal-article","article-title":"Dose-volume effect relationships for late rectal morbidity in patients treated with chemoradiation and mri-guided adaptive brachytherapy for locally advanced cervical cancer: results from the prospective multicenter embrace study","volume":"120","author":"Mazeron","year":"2016","journal-title":"Radiother. Oncol."},{"key":"pmbad3880bib14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1120\/jacmp.v5i4.1981","type":"journal-article","article-title":"A dose-volume-based tool for evaluating and ranking imrt treatment plans","volume":"5","author":"Miften","year":"2004","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"pmbad3880bib15","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1038\/s41598-018-37741-x","type":"journal-article","article-title":"A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning","volume":"9","author":"Nguyen","year":"2019","journal-title":"Sci. Rep."},{"key":"pmbad3880bib16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejmp.2021.12.009","type":"journal-article","article-title":"Deep reinforcement learning for treatment planning in high-dose-rate cervical brachytherapy","volume":"94","author":"Pu","year":"2022","journal-title":"Phys. Med."},{"key":"pmbad3880bib17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13014-018-1074-2","type":"journal-article","article-title":"High dose-rate tandem and ovoid brachytherapy in cervical cancer: dosimetric predictors of adverse events","volume":"13","author":"Romano","year":"2018","journal-title":"Radiat. Oncol."},{"key":"pmbad3880bib18","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","type":"journal-article","article-title":"Deep learning in medical imaging and radiation therapy","volume":"46","author":"Sahiner","year":"2019","journal-title":"Med. Phys."},{"key":"pmbad3880bib19","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1002\/mp.14712","type":"journal-article","article-title":"Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy","volume":"48","author":"Shen","year":"2021a","journal-title":"Med. Phys."},{"key":"pmbad3880bib20","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac09a2","type":"journal-article","article-title":"A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy","volume":"66","author":"Shen","year":"2021b","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib21","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab18bf","type":"journal-article","article-title":"Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer","volume":"64","author":"Shen","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib22","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1002\/mp.14114","type":"journal-article","article-title":"Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning","volume":"47","author":"Shen","year":"2020a","journal-title":"Med. Phys."},{"key":"pmbad3880bib23","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab6f51","type":"journal-article","article-title":"An introduction to deep learning in medical physics: advantages, potential, and challenges","volume":"65","author":"Shen","year":"2020b","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib24","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.radonc.2016.05.014","type":"journal-article","article-title":"Effect of tumor dose, volume and overall treatment time on local control after radiochemotherapy including mri guided brachytherapy of locally advanced cervical cancer","volume":"120","author":"Tanderup","year":"2016","journal-title":"Radiother. Oncol."},{"key":"pmbad3880bib25","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1016\/j.ijrobp.2014.11.014","type":"journal-article","article-title":"Evaluation of a knowledge-based planning solution for head and neck cancer","volume":"91","author":"Tol","year":"2015","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbad3880bib26","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/0360-3016(95)02120-5","type":"journal-article","article-title":"Evaluation and scoring of radiotherapy treatment plans using an artificial neural network","volume":"34","author":"Willoughby","year":"1996","journal-title":"Int. J. Rad. Oncol. Biol. Phys."},{"key":"pmbad3880bib27","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1088\/0031-9155\/46\/4\/313","type":"journal-article","article-title":"An optimization method for importance factors and beam weights based on genetic algorithms for radiotherapy treatment planning","volume":"46","author":"Wu","year":"2001","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib28","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1088\/0031-9155\/44\/10\/311","type":"journal-article","article-title":"Optimization of importance factors in inverse planning","volume":"44","author":"Xing","year":"1999","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib29","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1088\/0031-9155\/42\/1\/008","type":"journal-article","article-title":"Adaptive radiation therapy","volume":"42","author":"Yan","year":"1997","journal-title":"Phys. Med. Biol."},{"key":"pmbad3880bib30","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1016\/j.ijrobp.2020.10.019","type":"journal-article","article-title":"An interpretable planning bot for pancreas stereotactic body radiation therapy","volume":"109","author":"Zhang","year":"2021","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:05:08Z","timestamp":1778778308000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad3880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,17]]},"references-count":30,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,4,17]]},"published-print":{"date-parts":[[2024,5,7]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ad3880","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,17]]},"assertion":[{"value":"Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2023-11-03","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-03-26","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-04-17","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}