{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T22:59:22Z","timestamp":1771023562952,"version":"3.50.1"},"reference-count":67,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"vor","delay-in-days":64,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013362","name":"Cancer Research Foundation Switzerland","doi-asserted-by":"publisher","award":["KFS-5127-08-2020"],"award-info":[{"award-number":["KFS-5127-08-2020"]}],"id":[{"id":"10.13039\/501100013362","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013348","name":"Innosuisse Swiss Innovation Agency","doi-asserted-by":"publisher","award":["31274.1 IP-LS"],"award-info":[{"award-number":["31274.1 IP-LS"]}],"id":[{"id":"10.13039\/501100013348","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["phiro.science","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Physics and Imaging in Radiation Oncology"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1016\/j.phro.2025.100888","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T23:29:08Z","timestamp":1765063748000},"page":"100888","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy"],"prefix":"10.1016","volume":"36","author":[{"given":"Robert","family":"Poel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amith","family":"Kamath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ekin","family":"Ermi\u015f","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas","family":"Willmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elias","family":"R\u00fcfenacht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolaus","family":"Andratschke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Manser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel M.","family":"Aebersold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mauricio","family":"Reyes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.phro.2025.100888_b0005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.phro.2019.12.001","article-title":"Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy","volume":"13","author":"Vaassen","year":"2020","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0010","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.canrad.2018.01.008","article-title":"Contouring workload in adjuvant breast cancer radiotherapy","volume":"22","author":"Andrianarison","year":"2018","journal-title":"Cancer\/Radiotherapie"},{"key":"10.1016\/j.phro.2025.100888_b0015","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1200\/JCO.2015.63.9898","article-title":"Importance of radiation oncologist experience among patients with head-and-neck cancer treated with intensity-modulated radiation therapy","volume":"34","author":"Boero","year":"2016","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0020","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.ijrobp.2014.02.035","article-title":"Variability of target and normal structure delineation using multimodality imaging for radiation therapy of pancreatic cancer","volume":"89","author":"Dalah","year":"2014","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2025.100888_b0025","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1111\/j.1754-9485.2010.02192.x","article-title":"A review of methods of analysis in contouring studies for radiation oncology","volume":"54","author":"Jameson","year":"2010","journal-title":"J Med Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0030","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.radonc.2016.09.009","article-title":"Uncertainties in volume delineation in radiation oncology: a systematic review and recommendations for future studies","volume":"121","author":"Vinod","year":"2016","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0035","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1200\/JCO.2009.27.4498","article-title":"Critical impact of radiotherapy protocol compliance and quality in the treatment of advanced head and neck cancer: results from TROG 02.02","volume":"28","author":"Peters","year":"2010","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0040","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.radonc.2012.08.008","article-title":"QA makes a clinical trial stronger: evidence-based medicine in radiation therapy","volume":"105","author":"Weber","year":"2012","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0045","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.radonc.2012.04.002","article-title":"Quality assurance in the EORTC 22033-26033\/CE5 phase III randomized trial for low grade glioma: the digital individual case review","volume":"103","author":"Fairchild","year":"2012","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0050","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1200\/JCO.2014.56.5218","article-title":"Institutional clinical trial accrual volume and survival of patients with head and neck cancer","volume":"33","author":"Wuthrick","year":"2015","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0055","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.radonc.2019.04.012","article-title":"Impact of deviations in target volume delineation \u2013 time for a new RTQA approach?","volume":"137","author":"Cox","year":"2019","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0060","doi-asserted-by":"crossref","DOI":"10.1002\/mp.14030","article-title":"Strategies for effective physics plan and chart review in radiation therapy: report of AAPM task group 275","volume":"47","author":"Ford","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1118\/1.4871620","article-title":"Vision 20\/20: perspectives on automated image segmentation for radiotherapy","volume":"41","author":"Sharp","year":"2014","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0070","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/jmrs.65","article-title":"Reflections on the current status of commercial automated segmentation systems in clinical practice","volume":"61","author":"Sykes","year":"2014","journal-title":"J Med Radiat Sci"},{"key":"10.1016\/j.phro.2025.100888_b0075","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.semradonc.2019.02.001","article-title":"Advances in auto-segmentation","volume":"29","author":"Cardenas","year":"2019","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0080","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.clon.2021.12.003","article-title":"Machine Learning for auto-segmentation in radiotherapy planning","volume":"34","author":"Harrison","year":"2022","journal-title":"Clin Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0085","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fonc.2023.1213068","article-title":"A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy","volume":"13","author":"Doolan","year":"2023","journal-title":"Front Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0090","article-title":"Clinical implementation and evaluation of auto-segmentation tools for multi-site contouring in radiotherapy","volume":"28","author":"Heilemann","year":"2023","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0095","first-page":"1","article-title":"Real-world performance evaluation of commercial autocontouring software for head and neck cancer radiotherapy","author":"Young","year":"2025","journal-title":"Br J Radiol"},{"key":"10.1016\/j.phro.2025.100888_b0100","first-page":"1","article-title":"Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy","volume":"15","author":"Podobnik","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.phro.2025.100888_b0105","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.radonc.2020.09.008","article-title":"Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance","volume":"153","author":"Vandewinckele","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0110","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.clon.2020.10.017","article-title":"Structure and processes of existing practice in radiotherapy peer review: a systematic review of the literature","volume":"33","author":"Lewis","year":"2021","journal-title":"Clin Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0115","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.1002\/mp.16299","article-title":"Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation","volume":"50","author":"Duan","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0120","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1038\/s41571-020-0417-8","article-title":"Artificial intelligence in radiation oncology","volume":"17","author":"Huynh","year":"2020","journal-title":"Nat Rev Clin Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0125","doi-asserted-by":"crossref","DOI":"10.1186\/s13244-022-01276-7","article-title":"Quality assurance for automatically generated contours with additional deep learning","volume":"13","author":"Isaksson","year":"2022","journal-title":"Insights Imaging"},{"key":"10.1016\/j.phro.2025.100888_b0130","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnins.2020.00282","article-title":"Analyzing the quality and challenges of uncertainty estimations for brain tumor segmentation","volume":"14","author":"Jungo","year":"2020","journal-title":"Front Neurosci"},{"key":"10.1016\/j.phro.2025.100888_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102161","article-title":"The predictive value of segmentation metrics on dosimetry in organs at risk of the brain","volume":"73","author":"Poel","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.phro.2025.100888_b0140","doi-asserted-by":"crossref","first-page":"27","DOI":"10.59275\/j.melba.2023-dg1f","article-title":"Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient","volume":"2","author":"Kofler","year":"2023","journal-title":"Mach Learn Biomed Imaging"},{"key":"10.1016\/j.phro.2025.100888_b0145","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1136\/bmj.320.7237.768","article-title":"Human error: models and management","volume":"320","author":"Reason","year":"2000","journal-title":"Br Med J"},{"key":"10.1016\/j.phro.2025.100888_b0150","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1002\/mp.12142","article-title":"A Swiss cheese error detection method for real-time EPID-based quality assurance and error prevention","volume":"44","author":"Passarge","year":"2017","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0155","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.radonc.2020.10.027","article-title":"Dose prediction with deep learning for prostate cancer radiation therapy: model adaptation to different treatment planning practices","volume":"153","author":"Kandalan","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0160","doi-asserted-by":"crossref","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":"10.1016\/j.phro.2025.100888_b0165","doi-asserted-by":"crossref","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":"10.1016\/j.phro.2025.100888_b0170","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/acm2.13942","article-title":"TrDosePred: a deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy","volume":"24","author":"Hu","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0175","doi-asserted-by":"crossref","DOI":"10.3390\/cancers15174226","article-title":"Deep-Learning-based dose predictor for glioblastoma\u2013assessing the sensitivity and robustness for dose awareness in contouring","volume":"15","author":"Poel","year":"2023","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.phro.2025.100888_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.metrad.2024.100057","article-title":"A review of dose prediction methods for tumor radiation therapy","volume":"2","author":"Kui","year":"2024","journal-title":"Meta-Radiology"},{"key":"10.1016\/j.phro.2025.100888_b0185","doi-asserted-by":"crossref","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":"10.1016\/j.phro.2025.100888_b0190","doi-asserted-by":"crossref","DOI":"10.1186\/s13014-025-02634-7","article-title":"Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution","volume":"20","author":"Hou","year":"2025","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0195","first-page":"1","article-title":"CNN-based quality assurance for automatic segmentation of breast cancer in radiotherapy","volume":"10","author":"Chen","year":"2020","journal-title":"Front Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0200","article-title":"Stochastic segmentation networks: modelling spatially correlated aleatoric uncertainty","author":"Monteiro","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.phro.2025.100888_b0205","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12968-019-0523-x","article-title":"Automated quality control in image segmentation: application to the UK biobank cardiovascular magnetic resonance imaging study","volume":"21","author":"Robinson","year":"2019","journal-title":"J Cardiovasc Magn Reson"},{"key":"10.1016\/j.phro.2025.100888_b0210","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics)"},{"key":"10.1016\/j.phro.2025.100888_b0215","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.radonc.2015.12.003","article-title":"ESTRO-ACROP guideline \u201ctarget delineation of glioblastomas.","volume":"118","author":"Niyazi","year":"2016","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0220","first-page":"1","article-title":"A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy","volume":"5","author":"Poel","year":"2025","journal-title":"Nat Commun Med"},{"key":"10.1016\/j.phro.2025.100888_b0225","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/cancers15174226","article-title":"Deep learning-based dose predictor for glioblastoma \u2013 assessing the sensitivity and robustness for dose awareness in contouring","volume":"15","author":"Poel","year":"2023","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.phro.2025.100888_b0230","doi-asserted-by":"crossref","unstructured":"Kamath A, Poel R, Willmann J, Andratschkeand N, Reyes M. How Sensitive Are Deep Learning Based Radiotherapy Dose Prediction Models To Variability In Organs At Risk Segmentation? 2023 IEEE 20th Int. Symp. Biomed. Imaging, 2023, p. 1\u20134. https:\/\/doi.org\/10.1109\/ISBI53787.2023.10230559.","DOI":"10.1109\/ISBI53787.2023.10230559"},{"key":"10.1016\/j.phro.2025.100888_b0235","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1109\/TMI.2013.2251421","article-title":"Groupwise conditional random forests for automatic shape classification and contour quality assessment in radiotherapy planning","volume":"32","author":"McIntosh","year":"2013","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.phro.2025.100888_b0240","doi-asserted-by":"crossref","first-page":"5199","DOI":"10.1088\/0031-9155\/60\/13\/5199","article-title":"A framework for automated contour quality assurance in radiation therapy including adaptive techniques a framework for automated contour quality assurance in radiation therapy including adaptive techniques","volume":"60","author":"Altman","year":"2015","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.phro.2025.100888_b0245","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1118\/1.4906197","article-title":"Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: a general strategy","volume":"42","author":"Chen","year":"2015","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0250","doi-asserted-by":"crossref","first-page":"E668","DOI":"10.1016\/j.ijrobp.2016.06.2300","article-title":"Implementation of a machine learning\u2013based automatic contour quality assurance tool for online adaptive radiation therapy of prostate cancer","volume":"96","author":"Zhang","year":"2016","journal-title":"Int J Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0255","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1002\/mp.12835","article-title":"Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach","volume":"45","author":"Hui","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0260","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2017.2665165","article-title":"Reverse classification accuracy: predicting segmentation performance in the absence of ground truth","volume":"36","author":"Valindria","year":"2017","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.phro.2025.100888_b0265","doi-asserted-by":"crossref","unstructured":"Cubero L, Serrano J, Castelli J, De Crevoisier R, Acosta O, Pascau J. Exploring Uncertainty for Clinical Acceptability in Head and Neck Deep Learning-Based OAR Segmentation. Proc. - IEEE 20th Int. Symp. Biomed. Imaging, vol. 2023- April, 2023, p. 1\u20134. https:\/\/doi.org\/10.1109\/ISBI53787.2023.10230442.","DOI":"10.1109\/ISBI53787.2023.10230442"},{"key":"10.1016\/j.phro.2025.100888_b0270","doi-asserted-by":"crossref","unstructured":"Henderson EGA, Green AF, van Herk M, Vasquez Osorio EM. Automatic Identification of Segmentation Errors for Radiotherapy Using Geometric Learning. Med. Image Comput. Comput. Assist. Interv. \u2013 MICCAI vol 13435, 2022, p. 319\u201329. https:\/\/doi.org\/10.1007\/978-3-031-16443-9_31.","DOI":"10.1007\/978-3-031-16443-9_31"},{"key":"10.1016\/j.phro.2025.100888_b0275","unstructured":"Lee K, Lee H, Lee K, Shin J. Training confidence-calibrated classifiers for detecting out-of-distribution samples. 6th Int Conf Learn Represent ICLR 2018 - Conf Track Proc 2018:1\u201316."},{"key":"10.1016\/j.phro.2025.100888_b0280","unstructured":"Liang S, Li Y, Srikant R. Enhancing the reliability of out-of-distribution image detection in neural networks. 6th Int Conf Learn Represent ICLR 2018 - Conf Track Proc 2018:1\u201315."},{"key":"10.1016\/j.phro.2025.100888_b0285","doi-asserted-by":"crossref","first-page":"8209","DOI":"10.2147\/CMAR.S330249","article-title":"Automatic segmentation of clinical target volume and organs-at-risk for breast conservative radiotherapy using a convolutional neural network","volume":"13","author":"Liu","year":"2021","journal-title":"Cancer Manag Res"},{"key":"10.1016\/j.phro.2025.100888_b0290","doi-asserted-by":"crossref","first-page":"5549","DOI":"10.1002\/mp.14845","article-title":"OpenKBP: the open\u2010access knowledge\u2010based planning grand challenge and dataset","volume":"48","author":"Babier","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0295","first-page":"1","article-title":"Deep-neural network approaches for predicting 3D dose distribution in intensity-modulated radiotherapy of the brain tumors","volume":"25","author":"Irannejad","year":"2024","journal-title":"Radiat Oncol Phys"},{"key":"10.1016\/j.phro.2025.100888_b0300","first-page":"1","article-title":"Three-dimensional dose prediction based on deep convolutional neural networks for brain cancer in CyberKnife: accurate beam modelling of homogeneous tissue","volume":"6","author":"Miao","year":"2024","journal-title":"BJR Open"},{"key":"10.1016\/j.phro.2025.100888_b0305","first-page":"222","article-title":"Using probability maps for multi-organ automatic segmentation","author":"Joyseeree","year":"2013","journal-title":"Med Image Comput Comput Assist Interv \u2013 MICCAI"},{"key":"10.1016\/j.phro.2025.100888_b0310","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-016-0296-5","article-title":"Automatic liver segmentation based on appearance and context information","volume":"16","author":"Zheng","year":"2017","journal-title":"Biomed Eng Online"},{"key":"10.1016\/j.phro.2025.100888_b0315","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1361-6560\/abfce3","article-title":"Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet","volume":"66","author":"Li","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.phro.2025.100888_b0320","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1002\/acm2.13331","article-title":"A sensitivity analysis of probability maps in deep\u2010learning\u2010based anatomical segmentation","volume":"22","author":"Bice","year":"2021","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.phro.2025.100888_b0325","article-title":"Using spatial probability maps to highlight potential inaccuracies in deep learning-based contours: facilitating online adaptive radiation therapy","volume":"6","author":"van Rooij","year":"2021","journal-title":"Adv Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0330","article-title":"Leveraging network uncertainty to identify regions in rectal cancer clinical target volume auto-segmentations likely requiring manual edits","volume":"34","author":"Maruccio","year":"2025","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2025.100888_b0335","first-page":"1","article-title":"Automating high quality RT planning at scale","author":"Gao","year":"2025","journal-title":"Arxiv Prepr"}],"container-title":["Physics and Imaging in Radiation Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631625001939?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631625001939?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T21:36:36Z","timestamp":1768426596000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2405631625001939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":67,"alternative-id":["S2405631625001939"],"URL":"https:\/\/doi.org\/10.1016\/j.phro.2025.100888","relation":{},"ISSN":["2405-6316"],"issn-type":[{"value":"2405-6316","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy","name":"articletitle","label":"Article Title"},{"value":"Physics and Imaging in Radiation Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.phro.2025.100888","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.","name":"copyright","label":"Copyright"}],"article-number":"100888"}}