{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T01:57:37Z","timestamp":1793239057448,"version":"4.5.0"},"reference-count":130,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA257814"],"award-info":[{"award-number":["R01CA257814"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007313","name":"University of Texas MD Anderson Cancer Center","doi-asserted-by":"publisher","award":["K01DE030524"],"award-info":[{"award-number":["K01DE030524"]}],"id":[{"id":"10.13039\/100007313","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007313","name":"University of Texas MD Anderson Cancer Center","doi-asserted-by":"publisher","award":["NSF-2111147"],"award-info":[{"award-number":["NSF-2111147"]}],"id":[{"id":"10.13039\/100007313","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007313","name":"University of Texas MD Anderson Cancer Center","doi-asserted-by":"publisher","award":["R01CA195524"],"award-info":[{"award-number":["R01CA195524"]}],"id":[{"id":"10.13039\/100007313","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["P30CA016672"],"award-info":[{"award-number":["P30CA016672"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["K12CA088084"],"award-info":[{"award-number":["K12CA088084"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031152","name":"Center for Medicare and Medicaid Innovation","doi-asserted-by":"publisher","award":["NSF 1933369"],"award-info":[{"award-number":["NSF 1933369"]}],"id":[{"id":"10.13039\/100031152","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R25EB025787"],"award-info":[{"award-number":["R25EB025787"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000147","name":"Division of Civil, Mechanical and Manufacturing Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000147","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000072","name":"National Institute of Dental and Craniofacial Research","doi-asserted-by":"publisher","award":["R21DE031082"],"award-info":[{"award-number":["R21DE031082"]}],"id":[{"id":"10.13039\/100000072","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000072","name":"National Institute of Dental and Craniofacial Research","doi-asserted-by":"publisher","award":["R03DE033550"],"award-info":[{"award-number":["R03DE033550"]}],"id":[{"id":"10.13039\/100000072","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000072","name":"National Institute of Dental and Craniofacial Research","doi-asserted-by":"publisher","award":["R01DE025248"],"award-info":[{"award-number":["R01DE025248"]}],"id":[{"id":"10.13039\/100000072","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000072","name":"National Institute of Dental and Craniofacial Research","doi-asserted-by":"publisher","award":["R01DE028290-04S2"],"award-info":[{"award-number":["R01DE028290-04S2"]}],"id":[{"id":"10.13039\/100000072","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006093","name":"Patient-Centered Outcomes Research Institute","doi-asserted-by":"publisher","award":["PCS-1609-36195"],"award-info":[{"award-number":["PCS-1609-36195"]}],"id":[{"id":"10.13039\/100006093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thegreenjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Radiotherapy and Oncology"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.radonc.2024.110542","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T14:48:12Z","timestamp":1726584492000},"page":"110542","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":32,"special_numbering":"C","title":["Artificial intelligence uncertainty quantification in radiotherapy applications \u2212 A scoping review"],"prefix":"10.1016","volume":"201","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0503-0175","authenticated-orcid":false,"given":"Kareem A.","family":"Wahid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaphanlene Y.","family":"Kaffey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David P.","family":"Farris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laia","family":"Humbert-Vidan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy C.","family":"Moreno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathis","family":"Rasmussen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jintao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed A.","family":"Naser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tucker J.","family":"Netherton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stine","family":"Korreman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guha","family":"Balakrishnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clifton D.","family":"Fuller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Fuentes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Dohopolski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.radonc.2024.110542_b0005","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat Med"},{"key":"10.1016\/j.radonc.2024.110542_b0010","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/s42256-018-0004-1","article-title":"The need for uncertainty quantification in machine-assisted medical decision making","volume":"1","author":"Begoli","year":"2019","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.radonc.2024.110542_b0015","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s41746-021-00504-6","article-title":"Artificial intelligence sepsis prediction algorithm learns to say \u201cI don\u2019t know\u201d","volume":"4","author":"Shashikumar","year":"2021","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.radonc.2024.110542_b0020","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MSMC.2022.3150144","article-title":"The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process","volume":"8","author":"Abdar","year":"2022","journal-title":"IEEE Syst Man Cybern Mag"},{"key":"10.1016\/j.radonc.2024.110542_b0025","doi-asserted-by":"crossref","first-page":"e222217","DOI":"10.1148\/radiol.222217","article-title":"Quantifying uncertainty in deep learning of radiologic images","volume":"308","author":"Faghani","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.radonc.2024.110542_b0030","doi-asserted-by":"crossref","first-page":"753","DOI":"10.46747\/cfp.6710753","article-title":"Radiotherapy: Clinical pearls for primary care","volume":"67","author":"Chaput","year":"2021","journal-title":"Can Fam Physician"},{"key":"10.1016\/j.radonc.2024.110542_b0035","doi-asserted-by":"crossref","first-page":"939","DOI":"10.3390\/diagnostics14090939","article-title":"The integration of deep learning in radiotherapy: Exploring challenges, opportunities, and future directions through an umbrella review","volume":"14","author":"Lastrucci","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.radonc.2024.110542_b0040","doi-asserted-by":"crossref","first-page":"6537","DOI":"10.1002\/mp.15150","article-title":"Deep learning based synthetic-CT generation in radiotherapy and PET: A review","volume":"48","author":"Spadea","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0045","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.semradonc.2022.06.003","article-title":"Artificial intelligence for image registration in radiation oncology","volume":"32","author":"Teuwen","year":"2022","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0050","doi-asserted-by":"crossref","DOI":"10.3390\/cancers15174389","article-title":"Automatic segmentation with deep learning in radiotherapy","volume":"15","author":"Isaksson","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.radonc.2024.110542_b0055","article-title":"A review on application of deep learning algorithms in external beam radiotherapy automated treatment planning","volume":"10","author":"Wang","year":"2020","journal-title":"Front Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0060","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.canlet.2019.12.007","article-title":"Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges","volume":"471","author":"Huang","year":"2020","journal-title":"Cancer Lett"},{"key":"10.1016\/j.radonc.2024.110542_b0065","doi-asserted-by":"crossref","first-page":"e87","DOI":"10.1016\/j.clon.2021.12.002","article-title":"Deep learning for radiotherapy outcome prediction using dose data - a review","volume":"34","author":"Appelt","year":"2022","journal-title":"Clin Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0070","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.canrad.2023.05.001","article-title":"Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review","volume":"27","author":"Tan","year":"2023","journal-title":"Cancer Radiother"},{"key":"10.1016\/j.radonc.2024.110542_b0075","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.clon.2021.11.019","article-title":"Safe and ethical artificial intelligence in radiotherapy - lessons learned from the aviation industry","volume":"34","author":"Hallows","year":"2022","journal-title":"Clin Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0080","unstructured":"Heising L. Accelerating Implementation of Artificial Intelligence in Radiotherapy through Explainability. Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC, vol. 3554, Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V; 2023, p. 217\u201324."},{"key":"10.1016\/j.radonc.2024.110542_b0085","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","article-title":"Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods","volume":"110","author":"H\u00fcllermeier","year":"2021","journal-title":"Mach Learn"},{"key":"10.1016\/j.radonc.2024.110542_b0090","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.ijrobp.2020.04.011","article-title":"A systematic review of contouring guidelines in radiation oncology: analysis of frequency, methodology, and delivery of consensus recommendations","volume":"107","author":"Lin","year":"2020","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0095","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"key":"10.1016\/j.radonc.2024.110542_b0100","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.semradonc.2022.06.009","article-title":"Artificial intelligence for radiation oncology applications using public datasets","volume":"32","author":"Wahid","year":"2022","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0105","article-title":"ValUES: A framework for systematic validation of uncertainty estimation in semantic segmentation","author":"Kahl","year":"2024","journal-title":"arXiv [csCV]"},{"key":"10.1016\/j.radonc.2024.110542_b0110","series-title":"Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?","first-page":"2282","author":"Wimmer","year":"2023"},{"key":"10.1016\/j.radonc.2024.110542_b0115","series-title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","first-page":"1050","author":"Gal","year":"2016"},{"key":"10.1016\/j.radonc.2024.110542_b0120","unstructured":"Wilson AG. Deep ensembles as approximate Bayesian inference. Deep Ensembles as Approximate Bayesian Inference 2019. https:\/\/cims.nyu.edu\/\u223candrewgw\/deepensembles\/ (accessed April 8, 2024)."},{"key":"10.1016\/j.radonc.2024.110542_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107441","article-title":"Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013\u20132023)","volume":"165","author":"Seoni","year":"2023","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.radonc.2024.110542_b0130","first-page":"241","article-title":"Conformal prediction in clinical medical sciences","volume":"6","author":"Vazquez","year":"2022","journal-title":"Int J Healthc Inf Syst Inform"},{"key":"10.1016\/j.radonc.2024.110542_b0135","series-title":"On Calibration of Modern Neural Networks","first-page":"1321","author":"Guo","year":"2017"},{"key":"10.1016\/j.radonc.2024.110542_b0140","doi-asserted-by":"crossref","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","article-title":"Confidence calibration and predictive uncertainty estimation for deep medical image segmentation","volume":"39","author":"Mehrtash","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.radonc.2024.110542_b0145","article-title":"On calibrated model uncertainty in deep learning","author":"Ghoshal","year":"2022","journal-title":"arXiv [csLG]"},{"key":"10.1016\/j.radonc.2024.110542_b0150","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.semradonc.2022.06.001","article-title":"Uncertainty assessment for deep learning radiotherapy applications","volume":"32","author":"van den Berg","year":"2022","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0155","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000085","article-title":"Uncertainty-aware deep learning in healthcare: A scoping review","volume":"1","author":"Loftus","year":"2022","journal-title":"PLOS Digit Health"},{"key":"10.1016\/j.radonc.2024.110542_b0160","article-title":"A review of uncertainty estimation and its application in medical imaging","author":"Zou","year":"2023","journal-title":"arXiv [eessIV]"},{"key":"10.1016\/j.radonc.2024.110542_b0165","doi-asserted-by":"crossref","first-page":"e36427","DOI":"10.2196\/36427","article-title":"Uncertainty estimation in medical image classification: systematic review","volume":"10","author":"Kurz","year":"2022","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.radonc.2024.110542_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102830","article-title":"Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis","volume":"150","author":"Lambert","year":"2024","journal-title":"Artif Intell Med"},{"key":"10.1016\/j.radonc.2024.110542_b0175","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.radonc.2024.110542_b0180","doi-asserted-by":"crossref","first-page":"68","DOI":"10.5596\/c14-016","article-title":"Product Review: Covidence (Systematic Review Software)","volume":"35","author":"Babineau","year":"2014","journal-title":"J Can Health Libr Assoc"},{"key":"10.1016\/j.radonc.2024.110542_b0185","doi-asserted-by":"crossref","first-page":"520","DOI":"10.11124\/JBIES-22-00123","article-title":"Recommendations for the extraction, analysis, and presentation of results in scoping reviews","volume":"21","author":"Pollock","year":"2023","journal-title":"JBI Evid Synth"},{"key":"10.1016\/j.radonc.2024.110542_b0190","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1145\/1839676.1839701","article-title":"Relative status of journal and conference publications in computer science","volume":"53","author":"Freyne","year":"2010","journal-title":"Commun ACM"},{"key":"10.1016\/j.radonc.2024.110542_b0195","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12916-014-0241-z","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement","volume":"13","author":"Collins","year":"2015","journal-title":"BMC Med"},{"key":"10.1016\/j.radonc.2024.110542_b0200","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.radonc.2024.110542_b0205","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1088\/0031-9155\/60\/1\/233","article-title":"Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression","volume":"60","author":"Bukhari","year":"2015","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0210","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1118\/1.4915284","article-title":"Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk","volume":"42","author":"Lee","year":"2015","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0215","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","first-page":"3","article-title":"Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning","author":"Bragman","year":"2018"},{"key":"10.1016\/j.radonc.2024.110542_b0220","article-title":"Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation","author":"Jungo","year":"2018","journal-title":"arXiv [csCV]"},{"key":"10.1016\/j.radonc.2024.110542_b0225","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","first-page":"682","article-title":"On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation","author":"Jungo","year":"2018"},{"key":"10.1016\/j.radonc.2024.110542_b0230","first-page":"472","article-title":"Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10576","volume":"2018","author":"Ninomiya","year":"2018","journal-title":"SPIE"},{"key":"10.1016\/j.radonc.2024.110542_b0235","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aabd19","article-title":"Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation","volume":"63","author":"Qin","year":"2018","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0240","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","first-page":"765","article-title":"GDL-FIRE4D: Deep Learning-Based Fast 4D CT Image Registration","author":"Sentker","year":"2018"},{"key":"10.1016\/j.radonc.2024.110542_b0245","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.media.2019.07.005","article-title":"Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images","volume":"57","author":"Karimi","year":"2019","journal-title":"Med Image Anal"},{"key":"10.1016\/j.radonc.2024.110542_b0250","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1109\/TMI.2019.2902044","article-title":"Personalized radiotherapy design for glioblastoma: Integrating mathematical tumor models, multimodal scans, and bayesian inference","volume":"38","author":"Lipkova","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.radonc.2024.110542_b0255","doi-asserted-by":"crossref","first-page":"524","DOI":"10.3389\/fonc.2020.00524","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.radonc.2024.110542_b0260","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/abb71c","article-title":"Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty","volume":"65","author":"Dohopolski","year":"2020","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0265","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/abb0f9","article-title":"Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow","volume":"65","author":"Gustafsson","year":"2020","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0270","article-title":"Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours","volume":"7","author":"H\u00e4nsch","year":"2020","journal-title":"J Med Imaging (Bellingham)"},{"key":"10.1016\/j.radonc.2024.110542_b0275","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.radonc.2020.09.029","article-title":"Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy","volume":"153","author":"Maspero","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0280","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aba164","article-title":"Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network","volume":"65","author":"Nomura","year":"2020","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0285","first-page":"399","article-title":"Automatic online quality control of synthetic CTs. Medical Imaging 2020: Image Processing","volume":"11313","author":"van Harten","year":"2020","journal-title":"SPIE"},{"key":"10.1016\/j.radonc.2024.110542_b0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102101","article-title":"A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy","volume":"72","author":"Balagopal","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.radonc.2024.110542_b0295","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s11060-021-03762-2","article-title":"Quantitative mapping of individual voxels in the peritumoral region of IDH-wildtype glioblastoma to distinguish between tumor infiltration and edema","volume":"153","author":"Dasgupta","year":"2021","journal-title":"J Neurooncol"},{"key":"10.1016\/j.radonc.2024.110542_b0300","first-page":"66","article-title":"EFNet: evidence fusion network for tumor segmentation from PET-CT volumes","author":"Diao","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0305","doi-asserted-by":"crossref","first-page":"4353","DOI":"10.1002\/mp.15697","article-title":"A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with 81m Kr-gas SPECT ventilation imaging","volume":"49","author":"Kajikawa","year":"2022","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0310","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neucom.2021.01.135","article-title":"Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted loss","volume":"442","author":"Lei","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.radonc.2024.110542_b0315","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","first-page":"318","article-title":"Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency","author":"Luo","year":"2021"},{"key":"10.1016\/j.radonc.2024.110542_b0320","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.neucom.2020.06.146","article-title":"Automatic segmentation of gross target volume of nasopharynx cancer using ensemble of multiscale deep neural networks with spatial attention","volume":"438","author":"Mei","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.radonc.2024.110542_b0325","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/abe04f","article-title":"A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks","volume":"66","author":"Nguyen","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0330","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/abe956","article-title":"Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning","volume":"66","author":"Nomura","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0335","first-page":"66","article-title":"Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy","author":"Remy","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0340","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.radonc.2024.110542_b0345","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106419","article-title":"Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net","volume":"211","author":"Zhang","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.radonc.2024.110542_b0350","article-title":"Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician\u2019s perspective","author":"Dohopolski","year":"2022","journal-title":"arXiv [csLG]"},{"key":"10.1016\/j.radonc.2024.110542_b0355","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1002\/mp.15982","article-title":"An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning","volume":"50","author":"Li","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0360","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1002\/sim.9396","article-title":"Utility based approach in individualized optimal dose selection using machine learning methods","volume":"41","author":"Li","year":"2022","journal-title":"Stat Med"},{"key":"10.1016\/j.radonc.2024.110542_b0365","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1109\/JBHI.2021.3132173","article-title":"CT-guided survival prediction of esophageal cancer","volume":"26","author":"Lin","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.radonc.2024.110542_b0370","first-page":"67","article-title":"Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan","author":"Liu","year":"2022","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0375","article-title":"Conversion between CT and MRI images using diffusion and score-matching models","author":"Lyu","year":"2022","journal-title":"arXiv [eessIV]"},{"key":"10.1016\/j.radonc.2024.110542_b0380","first-page":"100","article-title":"Comparing Bayesian models for organ contouring in head and neck radiotherapy. Medical Imaging 2022: Image Processing","volume":"12032","author":"Mody","year":"2022","journal-title":"SPIE"},{"key":"10.1016\/j.radonc.2024.110542_b0385","series-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","first-page":"70","article-title":"Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty","author":"Mody","year":"2022"},{"key":"10.1016\/j.radonc.2024.110542_b0390","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106927","article-title":"Precision radiotherapy via information integration of expert human knowledge and AI recommendation to optimize clinical decision making","volume":"221","author":"Sun","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.radonc.2024.110542_b0395","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1002\/mp.16132","article-title":"Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option","volume":"50","author":"Wang","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0400","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.radonc.2022.05.016","article-title":"Uncertainty-guided man-machine integrated patient-specific quality assurance","volume":"173","author":"Yang","year":"2022","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0405","doi-asserted-by":"crossref","first-page":"e13725","DOI":"10.1002\/acm2.13725","article-title":"Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network","volume":"23","author":"Zabihollahy","year":"2022","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0410","first-page":"1","article-title":"IEEE 20th International Symposium on Biomedical Imaging (ISBI)","volume":"2023","author":"Cubero","year":"2023","journal-title":"IEEE"},{"key":"10.1016\/j.radonc.2024.110542_b0415","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acb9cf","article-title":"Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images","author":"De Biase","year":"2023","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0420","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102800","article-title":"CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation","volume":"86","author":"Ebadi","year":"2023","journal-title":"Med Image Anal"},{"key":"10.1016\/j.radonc.2024.110542_b0425","article-title":"Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy","author":"Galapon","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0430","article-title":"Clinically acceptable segmentation of organs at risk in cervical cancer radiation treatment from clinically available annotations","author":"Grewal","year":"2023","journal-title":"arXiv [eessIV]"},{"key":"10.1016\/j.radonc.2024.110542_b0435","first-page":"68","article-title":"Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes","author":"Huttinga","year":"2023","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0440","doi-asserted-by":"crossref","first-page":"e14248","DOI":"10.1002\/acm2.14248","article-title":"Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow","volume":"25","author":"Luan","year":"2024","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0445","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2023.109794","article-title":"Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation","volume":"186","author":"Min","year":"2023","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0450","article-title":"A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations","volume":"28","author":"Rodr\u00edguez Outeiral","year":"2023","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0455","article-title":"Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case","author":"Sahlsten","year":"2023","journal-title":"medRxiv"},{"key":"10.1016\/j.radonc.2024.110542_b0460","first-page":"68","article-title":"Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy","author":"Smolders","year":"2023","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0465","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1080\/0284186X.2023.2256967","article-title":"Proton range uncertainty caused by synthetic computed tomography generated with deep learning from pelvic magnetic resonance imaging","volume":"62","author":"Tian","year":"2023","journal-title":"Acta Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0470","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107939","article-title":"Deep learning-based outcome prediction using PET\/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer","volume":"244","author":"De Biase","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.radonc.2024.110542_b0475","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2023.110056","article-title":"Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour","volume":"191","author":"Li","year":"2024","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0480","first-page":"69","article-title":"Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy","author":"Rusanov","year":"2024","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0485","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1038\/s41431-020-0596-x","article-title":"Disruptive and avoidable: GDPR challenges to secondary research uses of data","volume":"28","author":"Peloquin","year":"2020","journal-title":"Eur J Hum Genet"},{"key":"10.1016\/j.radonc.2024.110542_b0490","first-page":"13","article-title":"Reproducibility in machine learning for health research: Still a ways to go","author":"McDermott","year":"2021","journal-title":"Sci Transl Med"},{"key":"10.1016\/j.radonc.2024.110542_b0495","doi-asserted-by":"crossref","first-page":"e220081","DOI":"10.1148\/ryai.220081","article-title":"Code and data sharing practices in the radiology artificial intelligence literature: A meta-research study","volume":"4","author":"Venkatesh","year":"2022","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.radonc.2024.110542_b0500","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1007\/s10278-023-00870-5","article-title":"Reproducibility of deep learning algorithms developed for medical imaging analysis: A systematic review","volume":"36","author":"Moassefi","year":"2023","journal-title":"J Digit Imaging"},{"key":"10.1016\/j.radonc.2024.110542_b0505","unstructured":"2023 NIH data management and sharing policy n.d. https:\/\/oir.nih.gov\/sourcebook\/intramural-program-oversight\/intramural-data-sharing\/2023-nih-data-management-sharing-policy (accessed May 2, 2024)."},{"key":"10.1016\/j.radonc.2024.110542_b0510","doi-asserted-by":"crossref","first-page":"12598","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","year":"2020","journal-title":"Sci Rep"},{"key":"10.1016\/j.radonc.2024.110542_b0515","doi-asserted-by":"crossref","unstructured":"Ger RB, Netherton TJ, Rhee DJ, Court LE, Yang J, Cardenas CE. Auto-contouring for Image-Guidance and Treatment Planning. In: El Naqa I, Murphy MJ, editors. Machine and Deep Learning in Oncology, Medical Physics and Radiology, Cham: Springer International Publishing; 2022, p. 231\u201393.","DOI":"10.1007\/978-3-030-83047-2_11"},{"key":"10.1016\/j.radonc.2024.110542_b0520","article-title":"NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions","author":"Rong","year":"2023","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.radonc.2024.110542_b0525","unstructured":"Riegel AC. Applications of Artificial Intelligence in Head and Neck Radiation Therapy n.d."},{"key":"10.1016\/j.radonc.2024.110542_b0530","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1053\/j.semnuclmed.2020.07.010","article-title":"Imaging for target delineation in head and neck cancer radiotherapy","volume":"51","author":"Jensen","year":"2021","journal-title":"Semin Nucl Med"},{"key":"10.1016\/j.radonc.2024.110542_b0535","first-page":"S11903","article-title":"E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation","volume":"10","author":"Lin","year":"2023","journal-title":"J Med Imaging (Bellingham)"},{"key":"10.1016\/j.radonc.2024.110542_b0540","article-title":"Automated Contouring and Planning in Radiation Therapy: What Is \u201cClinically Acceptable\u201d?","volume":"13","author":"Baroudi","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.radonc.2024.110542_b0545","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acc309","article-title":"Accurate segmentation of head and neck radiotherapy CT scans with 3D CNNs: consistency is key","volume":"68","author":"Henderson","year":"2023","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0550","article-title":"Evolving horizons in radiotherapy auto-contouring: Distilling insights, embracing data-centric frameworks, and moving beyond geometric quantification","volume":"101521","author":"Wahid","year":"2024","journal-title":"Adv Radiation Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0555","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.radonc.2024.110542_b0560","doi-asserted-by":"crossref","first-page":"7998","DOI":"10.1007\/s00330-022-08784-6","article-title":"Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)","volume":"32","author":"Kelly","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.radonc.2024.110542_b0565","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ac2206","article-title":"The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients","volume":"66","author":"Fang","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2024.110542_b0570","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1007\/s11548-020-02175-2","article-title":"Training of head and neck segmentation networks with shape prior on small datasets","volume":"15","author":"Tappeiner","year":"2020","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10.1016\/j.radonc.2024.110542_b0575","doi-asserted-by":"crossref","first-page":"137","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.radonc.2024.110542_b0580","article-title":"The Peril of Popular Deep Learning Uncertainty Estimation Methods","author":"Liu","year":"2021","journal-title":"arXiv [csLG]"},{"key":"10.1016\/j.radonc.2024.110542_b0585","article-title":"Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data","author":"Ulmer","year":"2020","journal-title":"arXiv [csLG]"},{"key":"10.1016\/j.radonc.2024.110542_b0590","doi-asserted-by":"crossref","unstructured":"Czolbe S, Arnavaz K, Krause O, Feragen A. Is Segmentation Uncertainty Useful? Information Processing in Medical Imaging, Springer International Publishing; 2021, p. 715\u201326.","DOI":"10.1007\/978-3-030-78191-0_55"},{"key":"10.1016\/j.radonc.2024.110542_b0595","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","first-page":"514","article-title":"Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation","author":"Kushibar","year":"2022"},{"key":"10.1016\/j.radonc.2024.110542_b0600","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2023.104019","article-title":"DEep Evidential Doctor","volume":"325","author":"Ashfaq","year":"2023","journal-title":"Artif Intell"},{"key":"10.1016\/j.radonc.2024.110542_b0605","doi-asserted-by":"crossref","unstructured":"Kutiel G, Cohen R, Elad M, Freedman D, Rivlin E. Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging. Trustworthy Machine Learning for Healthcare, Springer Nature Switzerland; 2023, p. 163\u201376.","DOI":"10.1007\/978-3-031-39539-0_14"},{"key":"10.1016\/j.radonc.2024.110542_b0610","first-page":"2022","article-title":"Challenge on quantifying uncertainty in brain tumor segmentation - analysis of ranking scores and benchmarking results","volume":"2022","author":"Mehta","year":"2020","journal-title":"J Mach Learn Biomed Imaging"},{"key":"10.1016\/j.radonc.2024.110542_b0615","article-title":"Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty","author":"Monteiro","year":"2020","journal-title":"arXiv [csCV]"},{"key":"10.1016\/j.radonc.2024.110542_b0620","doi-asserted-by":"crossref","first-page":"e220079","DOI":"10.1148\/ryai.220079","article-title":"Impact of different artificial intelligence user interfaces on lung nodule and mass detection on chest radiographs","volume":"5","author":"Tang","year":"2023","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.radonc.2024.110542_b0625","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41746-021-00385-9","article-title":"Do as AI say: susceptibility in deployment of clinical decision-aids","volume":"4","author":"Gaube","year":"2021","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.radonc.2024.110542_b0630","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2021.103456","article-title":"Show or suppress? Managing input uncertainty in machine learning model explanations","volume":"294","author":"Wang","year":"2021","journal-title":"Artif Intell"},{"key":"10.1016\/j.radonc.2024.110542_b0635","article-title":"Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence","author":"Le","year":"2022","journal-title":"arXiv [csLG]"},{"key":"10.1016\/j.radonc.2024.110542_b0640","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.inffus.2023.03.008","article-title":"A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion","volume":"96","author":"Albahri","year":"2023","journal-title":"Inf Fusion"},{"key":"10.1016\/j.radonc.2024.110542_b0645","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2023.109747","article-title":"An investigation into the risk of population bias in deep learning autocontouring","volume":"186","author":"McQuinlan","year":"2023","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2024.110542_b0650","unstructured":"Mensah GB. Artificial intelligence and ethics: A comprehensive review of bias mitigation, transparency, and accountability in AI systems 2023. Doi: 10.13140\/RG.2.2.23381.19685\/1."}],"container-title":["Radiotherapy and Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814024035205?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814024035205?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:21:00Z","timestamp":1777504860000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167814024035205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":130,"alternative-id":["S0167814024035205"],"URL":"https:\/\/doi.org\/10.1016\/j.radonc.2024.110542","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.05.13.24307226","asserted-by":"object"}]},"ISSN":["0167-8140"],"issn-type":[{"value":"0167-8140","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence uncertainty quantification in radiotherapy applications \u2212 A scoping review","name":"articletitle","label":"Article Title"},{"value":"Radiotherapy and Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.radonc.2024.110542","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"110542"}}