{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:06:53Z","timestamp":1784182013292,"version":"3.55.0"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Radiation and Isotopes"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.apradiso.2026.112797","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T23:54:00Z","timestamp":1783468440000},"page":"112797","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MRI-based gross tumor volume delineation in high-grade glioma using a deep convolutional neural network"],"prefix":"10.1016","volume":"236","author":[{"given":"Sogand","family":"Sadeghi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iman","family":"Shokatian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Siavashpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Vafaei Sadr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afshin","family":"Rakhsha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6952-1152","authenticated-orcid":false,"given":"Somayeh","family":"Gholami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.apradiso.2026.112797_bib1","first-page":"1","article-title":"Advanced normalization tools (ANTS)","author":"Avants","year":"2008","journal-title":"Insight J"},{"key":"10.1016\/j.apradiso.2026.112797_bib2","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"key":"10.1016\/j.apradiso.2026.112797_bib3","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"key":"10.1016\/j.apradiso.2026.112797_bib4","article-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge","author":"Bakas","year":"2018","journal-title":"arXiv preprint arXiv:181102629"},{"key":"10.1016\/j.apradiso.2026.112797_bib5","doi-asserted-by":"crossref","first-page":"186","DOI":"10.18383\/j.tom.2019.00026","article-title":"A fully automated deep learning network for brain tumor segmentation","volume":"6","author":"Bangalore","year":"2020","journal-title":"Tomography"},{"key":"10.1016\/j.apradiso.2026.112797_bib6","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.phro.2020.06.006","article-title":"External validation of deep learning-based contouring of head and neck organs at risk","volume":"15","author":"Brunenberg","year":"2020","journal-title":"Phys. Imag. Radiation Oncol."},{"key":"10.1016\/j.apradiso.2026.112797_bib7","series-title":"International MICCAI Brainlesion Workshop","first-page":"358","article-title":"S3D-UNet: separable 3D U-Net for brain tumor segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.apradiso.2026.112797_bib8","series-title":"Imaging of Brain Tumors with Histological Correlations","article-title":"Imaging modalities in brain tumors","author":"Drevelegas","year":"2011"},{"key":"10.1016\/j.apradiso.2026.112797_bib9","doi-asserted-by":"crossref","first-page":"N337","DOI":"10.1088\/0031-9155\/55\/11\/N04","article-title":"Comparison of DVH data from multiple radiotherapy treatment planning systems","volume":"55","author":"Ebert","year":"2010","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.apradiso.2026.112797_bib10","first-page":"1","article-title":"Focusing on brain tumours and brain metastasis","volume":"20","year":"2020","journal-title":"Nat. Rev. Cancer"},{"key":"10.1016\/j.apradiso.2026.112797_bib11","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1007\/s12094-018-1896-2","article-title":"New physical approaches to treat cancer stem cells: a review","volume":"20","author":"Ghaffari","year":"2018","journal-title":"Clin. Transl. Oncol."},{"key":"10.1016\/j.apradiso.2026.112797_bib12","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.apradiso.2026.112797_bib13","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1109\/42.650883","article-title":"Markov random field segmentation of brain MR images","volume":"16","author":"Held","year":"1997","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.apradiso.2026.112797_bib14","doi-asserted-by":"crossref","first-page":"92615","DOI":"10.1109\/ACCESS.2019.2927433","article-title":"Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.apradiso.2026.112797_bib15","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.procs.2016.09.407","article-title":"Review of MRI-based brain tumor image segmentation using deep learning methods","volume":"102","author":"I\u015f\u0131n","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.apradiso.2026.112797_bib16","series-title":"International MICCAI Brainlesion Workshop","first-page":"234","article-title":"No new-net","author":"Isensee","year":"2018"},{"key":"10.1016\/j.apradiso.2026.112797_bib17","series-title":"International MICCAI Brainlesion Workshop","first-page":"287","article-title":"Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge","author":"Isensee","year":"2017"},{"key":"10.1016\/j.apradiso.2026.112797_bib18","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.apradiso.2026.112797_bib19","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics10110959","article-title":"A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy Planning-A retrospective multicenter study","volume":"10","author":"Kiljunen","year":"2020","journal-title":"Diagnostics"},{"key":"10.1016\/j.apradiso.2026.112797_bib20","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2214\/AJR.18.20218","article-title":"State of the art: machine learning applications in glioma imaging","volume":"212","author":"Lotan","year":"2018","journal-title":"Am. J. Roentgenol."},{"key":"10.1016\/j.apradiso.2026.112797_bib21","article-title":"Deep learning for brain tumor segmentation: a survey of state-of-the-art","volume":"7","author":"Magadza","year":"2021","journal-title":"J. Imag."},{"key":"10.1016\/j.apradiso.2026.112797_bib22","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.amjmed.2017.12.039","article-title":"Brain tumors","volume":"131","author":"McFaline-Figueroa","year":"2018","journal-title":"Am. J. Med."},{"key":"10.1016\/j.apradiso.2026.112797_bib23","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.apradiso.2026.112797_bib24","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.apradiso.2026.112797_bib25","doi-asserted-by":"crossref","first-page":"559","DOI":"10.31661\/jbpe.v0i0.2008-1153","article-title":"Transfer learning-based automatic detection of coronavirus disease 2019 (COVID-19) from chest X-ray images","volume":"10","author":"Mohammadi","year":"2020","journal-title":"J. Biomed. Phys. Eng."},{"key":"10.1016\/j.apradiso.2026.112797_bib26","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.radonc.2021.03.030","article-title":"Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer","volume":"159","author":"Mohammadi","year":"2021","journal-title":"Radiother. Oncol."},{"key":"10.1016\/j.apradiso.2026.112797_bib27","doi-asserted-by":"crossref","DOI":"10.3390\/brainsci10020118","article-title":"Brain tumor analysis empowered with deep learning: a review, taxonomy, and future challenges","volume":"10","author":"Nadeem","year":"2020","journal-title":"Brain Sci."},{"key":"10.1016\/j.apradiso.2026.112797_bib28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.radonc.2015.12.003","article-title":"ESTRO-ACROP guideline \"target delineation of glioblastomas\"","volume":"118","author":"Niyazi","year":"2016","journal-title":"Radiother. Oncol."},{"key":"10.1016\/j.apradiso.2026.112797_bib29","article-title":"Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI","author":"Pei","year":"2017","journal-title":"Proc. SPIE-Int. Soc. Opt. Eng."},{"key":"10.1016\/j.apradiso.2026.112797_bib30","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"35","author":"Pereira","year":"2016","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.apradiso.2026.112797_bib31","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.breast.2019.11.011","article-title":"Winter is over: the use of artificial intelligence to individualise radiation therapy for breast cancer","volume":"49","author":"Poortmans","year":"2020","journal-title":"Breast"},{"key":"10.1016\/j.apradiso.2026.112797_bib32","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/JBHI.2018.2874033","article-title":"Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks","volume":"23","author":"Razzak","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.apradiso.2026.112797_bib33","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.apradiso.2026.112797_bib34","doi-asserted-by":"crossref","first-page":"31","DOI":"10.5114\/pjr.2023.124434","article-title":"Fully automated clinical target volume segmentation for glioblastoma radiotherapy using a\u00a0deep convolutional neural network","volume":"88","author":"Sadeghi","year":"2023","journal-title":"Pol. J. Radiol."},{"issue":"5","key":"10.1016\/j.apradiso.2026.112797_bib35","doi-asserted-by":"crossref","first-page":"052001","DOI":"10.1088\/2057-1976\/ac14d0","article-title":"A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy","volume":"7","author":"Sadeghi","year":"2021","journal-title":"Biomed. Phys. Eng. Express"},{"key":"10.1016\/j.apradiso.2026.112797_bib36","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20201263","article-title":"Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images","volume":"94","author":"Salehi","year":"2021","journal-title":"Br. J. Radiol."},{"key":"10.1016\/j.apradiso.2026.112797_bib37","doi-asserted-by":"crossref","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","article-title":"Review of deep learning algorithms and architectures","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.apradiso.2026.112797_bib38","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s12021-014-9245-2","article-title":"Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR","volume":"13","author":"Tustison","year":"2015","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.apradiso.2026.112797_bib39","series-title":"International MICCAI Brainlesion Workshop","first-page":"131","article-title":"3d u-net based brain tumor segmentation and survival days prediction","author":"Wang","year":"2019"},{"key":"10.1016\/j.apradiso.2026.112797_bib40","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3389\/fncom.2019.00056","article-title":"Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation","volume":"13","author":"Wang","year":"2019","journal-title":"Front. Comput. Neurosci."},{"key":"10.1016\/j.apradiso.2026.112797_bib41","doi-asserted-by":"crossref","DOI":"10.3389\/fncom.2019.00056","article-title":"Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation","volume":"13","author":"Wang","year":"2019","journal-title":"Front. Comput. Neurosci."},{"key":"10.1016\/j.apradiso.2026.112797_bib42","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1109\/TITS.2015.2482461","article-title":"Towards real-time traffic sign detection and classification","volume":"17","author":"Yang","year":"2016","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"10.1016\/j.apradiso.2026.112797_bib43","article-title":"AResU-Net: attention residual U-Net for brain tumor segmentation","volume":"12","author":"Zhang","year":"2020","journal-title":"Symmetry"}],"container-title":["Applied Radiation and Isotopes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0969804326003817?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0969804326003817?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:25:38Z","timestamp":1784179538000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0969804326003817"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":43,"alternative-id":["S0969804326003817"],"URL":"https:\/\/doi.org\/10.1016\/j.apradiso.2026.112797","relation":{},"ISSN":["0969-8043"],"issn-type":[{"value":"0969-8043","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MRI-based gross tumor volume delineation in high-grade glioma using a deep convolutional neural network","name":"articletitle","label":"Article Title"},{"value":"Applied Radiation and Isotopes","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.apradiso.2026.112797","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112797"}}