{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T17:14:13Z","timestamp":1794330853632,"version":"4.9.0"},"reference-count":29,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"crossref","award":["183757"],"award-info":[{"award-number":["183757"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Chair in Medical Imaging at the Joint Department of Medical Imaging at the University Health Network"},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["DGECR-2022-00137"],"award-info":[{"award-number":["DGECR-2022-00137"]}],"id":[{"id":"10.13039\/501100000038","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":[[2025,3,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective<\/jats:italic>. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment planning as separate tasks. Moreover in DL, the contouring and dose prediction tasks for automated treatment planning are done independently. <jats:italic>Approach<\/jats:italic>. In this study, we applied the multi-task learning (MTL) approach in order to seamlessly integrate automated contouring and voxel-based dose prediction tasks, as MTL can leverage common information between the two tasks and be able to increase the efficiency of the automated tasks. We developed our MTL framework using the two datasets: in-house prostate cancer dataset and the publicly available head and neck cancer dataset, OpenKBP. <jats:italic>Main results<\/jats:italic>. Compared to the sequential DL contouring and treatment planning tasks, our proposed method using MTL improved the mean absolute difference of dose volume histogram metrics of prostate and head and neck sites by 19.82% and 16.33%, respectively. Our MTL model for automated contouring and dose prediction tasks demonstrated enhanced dose prediction performance while maintaining or sometimes even improving the contouring accuracy. Compared to the baseline automated contouring model with the Dice score coefficients of 0.818 for prostate and 0.674 for head and neck datasets, our MTL approach achieved average scores of 0.824 and 0.716 for these datasets, respectively. <jats:italic>Significance<\/jats:italic>. Our study highlights the potential of the proposed automated contouring and planning using MTL to support the development of efficient and accurate automated treatment planning for radiotherapy.<\/jats:p>","DOI":"10.1088\/1361-6560\/adb23d","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T22:58:07Z","timestamp":1738709887000},"page":"055007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-task learning for automated contouring and dose prediction in radiotherapy"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6482-9561","authenticated-orcid":true,"given":"Sangwook","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1487-648X","authenticated-orcid":true,"given":"Aly","family":"Khalifa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4176-8457","authenticated-orcid":true,"given":"Thomas G","family":"Purdie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1371-1250","authenticated-orcid":true,"given":"Chris","family":"McIntosh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"pmbadb23dbib1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acf023","article-title":"Geometric evaluations of CT and MRI based deep learning segmentation for brain oars in radiotherapy","volume":"68","author":"Alzahrani","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"pmbadb23dbib2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037","article-title":"Multi-task deep learning based CT imaging analysis for covid-19 pneumonia: classification and segmentation","volume":"126","author":"Amyar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"pmbadb23dbib3","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac8044","article-title":"OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines","volume":"67","author":"Babier","year":"2022","journal-title":"Phys. Med. Biol."},{"key":"pmbadb23dbib4","doi-asserted-by":"publisher","first-page":"5549","DOI":"10.1002\/mp.14845","article-title":"OpenKBP: The open-access knowledge-based planning grand challenge and dataset","volume":"48","author":"Babier","year":"2021","journal-title":"Med. Phys."},{"key":"pmbadb23dbib5","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"pmbadb23dbib6","first-page":"457","article-title":"Multi-task attention-based semi-supervised learning for medical image segmentation","volume":"vol 22","author":"Chen","year":"2019"},{"key":"pmbadb23dbib7","first-page":"191","article-title":"Diffdp: Radiotherapy dose prediction via a diffusion model","author":"Feng","year":"2023"},{"key":"pmbadb23dbib8","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1109\/CVPR52729.2023.00076","article-title":"Flexible-cm gan: Towards precise 3d dose prediction in radiotherapy","author":"Gao","year":"2023"},{"key":"pmbadb23dbib9","doi-asserted-by":"publisher","first-page":"e282","DOI":"10.1016\/j.prro.2022.12.003","article-title":"Deep learning\u2013based dose prediction for automated, individualized quality assurance of head and neck radiation therapy plans","volume":"13","author":"Gronberg","year":"2023","journal-title":"Pract. Radiat. Oncol."},{"key":"pmbadb23dbib10","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1251132","article-title":"Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data","volume":"13","author":"Gu","year":"2023","journal-title":"Front. Oncol."},{"key":"pmbadb23dbib11","doi-asserted-by":"crossref","DOI":"10.59275\/j.melba.2021-d8a3","article-title":"Generalized multi-task learning from substantially unlabeled multi-source medical image data","author":"Haque","year":"2021"},{"key":"pmbadb23dbib12","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"pmbadb23dbib13","first-page":"1","article-title":"Mask-free radiotherapy dose prediction via multi-task learning","author":"Jiao","year":"2022"},{"key":"pmbadb23dbib14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102035","article-title":"Loss odyssey in medical image segmentation","volume":"71","author":"Jun","year":"2021","journal-title":"Med. Image Anal."},{"key":"pmbadb23dbib15","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1093\/bjr\/tqae076","article-title":"Conversion of single-energy CT to parametric maps of dual-energy CT using convolutional neural network","volume":"97","author":"Kim","year":"2024","journal-title":"Br. J. Radiol."},{"key":"pmbadb23dbib16","first-page":"119","article-title":"Cross-task attention network: Improving multi-task learning for medical imaging applications","author":"Kim","year":"2023"},{"key":"pmbadb23dbib17","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1109\/CVPR.2019.00197","article-title":"End-to-end multi-task learning with attention","author":"Liu","year":"2019"},{"key":"pmbadb23dbib18","doi-asserted-by":"publisher","first-page":"5574","DOI":"10.1002\/mp.15034","article-title":"A cascade 3D U-Net for dose prediction in radiotherapy","volume":"48","author":"Liu","year":"2021","journal-title":"Med. Phys."},{"key":"pmbadb23dbib19","doi-asserted-by":"publisher","first-page":"1972","DOI":"10.1002\/mp.13490","article-title":"A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy","volume":"46","author":"Liu","year":"2019","journal-title":"Med. Phys."},{"key":"pmbadb23dbib20","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.1061024","article-title":"A human-in-the-loop based bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy","volume":"12","author":"Luo","year":"2022","journal-title":"Front. Oncol."},{"key":"pmbadb23dbib21","doi-asserted-by":"publisher","first-page":"3932","DOI":"10.1002\/mp.17115","article-title":"Multimodal radiotherapy dose prediction using a multi-task deep learning model","volume":"51","author":"Maniscalco","year":"2024","journal-title":"Med. Phys."},{"key":"pmbadb23dbib22","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1038\/s41591-021-01359-w","article-title":"Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer","volume":"27","author":"McIntosh","year":"2021","journal-title":"Nat. Med."},{"key":"pmbadb23dbib23","first-page":"478","article-title":"Deep learning for multi-task medical image segmentation in multiple modalities","author":"Moeskops","year":"2016"},{"key":"pmbadb23dbib24","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab039b","article-title":"3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture","volume":"64","author":"Nguyen","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"pmbadb23dbib25","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1038\/s41598-018-37741-x","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":"pmbadb23dbib26","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"pmbadb23dbib27","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110332","article-title":"Generalizability of deep learning in organ-at-risk segmentation: A transfer learning study in cervical brachytherapy","volume":"197","author":"Ruiyan","year":"2024","journal-title":"Radiother. Oncol."},{"key":"pmbadb23dbib28","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500430","article-title":"A transformer-embedded multi-task model for dose distribution prediction","volume":"33","author":"Wen","year":"2023","journal-title":"Int. J. Neural Syst."},{"key":"pmbadb23dbib29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106496","article-title":"Multi-task deep learning for medical image computing and analysis: a review","volume":"153","author":"Zhao","year":"2023","journal-title":"Comput. Biol. Med."}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T06:18:36Z","timestamp":1739859516000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/adb23d"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,18]]},"references-count":29,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,2,18]]},"published-print":{"date-parts":[[2025,3,2]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/adb23d","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,18]]},"assertion":[{"value":"Multi-task learning for automated contouring and dose prediction in radiotherapy","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 2025 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-11-30","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-02-04","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-02-18","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}