{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T23:21:14Z","timestamp":1769642474066,"version":"3.49.0"},"reference-count":44,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,28]],"date-time":"2027-01-28T00:00:00Z","timestamp":1801094400000},"content-version":"am","delay-in-days":392,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100030735","name":"Kyorin University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100030735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","meddos.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Dosimetry"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.meddos.2025.07.006","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T13:07:16Z","timestamp":1755695236000},"page":"57-66","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional neural network-based prediction of gamma pass rate in volumetric modulated arc therapy using 3d fluence maps and complexity metrics"],"prefix":"10.1016","volume":"51","author":[{"given":"Hiromi","family":"Enomoto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukio","family":"Fujita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9224-259X","authenticated-orcid":false,"given":"Saki","family":"Matsumoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takehiro","family":"Nakai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujiro","family":"Nakajima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0620-9134","authenticated-orcid":false,"given":"Takeshi","family":"Ebara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.meddos.2025.07.006_bib0001","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1259\/bjr\/22373346","article-title":"Volumetric modulated arc therapy: a review of current literature and clinical use in practice","volume":"84","author":"Teoh","year":"2011","journal-title":"BJR"},{"issue":"8","key":"10.1016\/j.meddos.2025.07.006_bib0002","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1118\/1.1591194","article-title":"Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT subcommittee of the AAPM radiation therapy committee","volume":"30","author":"Ezzell","year":"2003","journal-title":"Med Phys"},{"issue":"11","key":"10.1016\/j.meddos.2025.07.006_bib0003","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1118\/1.3238104","article-title":"IMRT commissioning: multiple institution planning and dosimetry comparisons, a report from AAPM Task Group 119","volume":"36","author":"Ezzell","year":"2009","journal-title":"Med Phys"},{"issue":"9","key":"10.1016\/j.meddos.2025.07.006_bib0004","doi-asserted-by":"crossref","first-page":"4197","DOI":"10.1118\/1.3190392","article-title":"Task Group 142 report: quality assurance of medical accelerators","volume":"36","author":"Klein","year":"2009","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0005","doi-asserted-by":"crossref","DOI":"10.1002\/mp.14992","article-title":"AAPM Task Group 198 report: an implementation guide for TG 142 quality assurance of medical accelerators","volume":"48","author":"Hanley","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0006","doi-asserted-by":"crossref","DOI":"10.1002\/mp.12810","article-title":"Tolerance limits and methodologies for IMRT measurement-based verification QA: recommendations of AAPM Task Group No. 218","volume":"45","author":"Miften","year":"2018","journal-title":"Med Phys"},{"issue":"3","key":"10.1016\/j.meddos.2025.07.006_bib0007","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.semradonc.2019.02.004","article-title":"Practical clinical workflows for online and offline adaptive radiation therapy","volume":"29","author":"Green","year":"2019","journal-title":"Semin Radiat Oncol"},{"issue":"2","key":"10.1016\/j.meddos.2025.07.006_bib0008","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1002\/mp.13338","article-title":"Deep learning for patient-specific quality assurance: identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks","volume":"46","author":"Nyflot","year":"2019","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0009","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ejmp.2020.03.022","article-title":"Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy","volume":"73","author":"Kimura","year":"2020","journal-title":"Phys Medica"},{"issue":"4","key":"10.1016\/j.meddos.2025.07.006_bib0010","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1002\/mp.14014","article-title":"Jayesh K. Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network","volume":"47","author":"Osman","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0011","doi-asserted-by":"crossref","first-page":"4769","DOI":"10.1002\/mp.15031","article-title":"Error detection model developed using a multi-task convolutional neural network in patient-specific quality assurance for volumetric-modulated arc therapy","volume":"48","author":"Kimura","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0012","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1002\/mp.14699","article-title":"Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy","volume":"48","author":"Sakai","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0013","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.phro.2022.03.004","article-title":"A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry","volume":"22","author":"Bedford","year":"2022","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.meddos.2025.07.006_bib0014","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1093\/jrr\/rrad028","article-title":"Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy","volume":"64","author":"Kimura","year":"2023","journal-title":"J Radiat Res"},{"key":"10.1016\/j.meddos.2025.07.006_bib0015","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14136","article-title":"Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy","volume":"24","author":"Nakamura","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0016","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.1118\/1.4953835","article-title":"A mathematical framework for virtual IMRT QA using machine learning","volume":"43","author":"Valdes","year":"2016","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0017","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1002\/acm2.12161","article-title":"IMRT QA using machine learning: a multi-institutional validation","volume":"18","author":"Valdes","year":"2017","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0018","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.1002\/mp.13112","article-title":"A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance","volume":"45","author":"Tomori","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0019","doi-asserted-by":"crossref","first-page":"2672","DOI":"10.1002\/mp.12890","article-title":"Deep nets vs expert designed features in medical physics: an IMRT QA case study","volume":"45","author":"Interian","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0020","doi-asserted-by":"crossref","first-page":"3823","DOI":"10.1002\/mp.13669","article-title":"Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning","volume":"46","author":"Ono","year":"2019","journal-title":"Med Phys"},{"issue":"4","key":"10.1016\/j.meddos.2025.07.006_bib0021","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.ijrobp.2019.07.049","article-title":"Machine learning for patient-specific quality assurance of VMAT: prediction and classification accuracy","volume":"105","author":"Li","year":"2019","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0022","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.radonc.2020.07.031","article-title":"Improvement of prediction and classification performance for gamma passing rate by using plan complexity and dosiomics features","volume":"153","author":"Hirashima","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.meddos.2025.07.006_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2020.100292","article-title":"Application and comparison of machine learning models for predicting quality assurance outcomes in radiation therapy treatment planning","volume":"18","author":"Wall","year":"2020","journal-title":"Informatics Med Unlocked"},{"key":"10.1016\/j.meddos.2025.07.006_bib0024","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1002\/mp.14682","article-title":"Systematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy","volume":"48","author":"Tomori","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0025","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13824","article-title":"Using machine learning to predict gamma passing rate in volumetric-modulated arc therapy treatment plans","volume":"24","author":"Salari","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejmp.2023.102593","article-title":"Multicentric evaluation of a machine learning model to streamline the radiotherapy patient-specific quality assurance process","volume":"110","author":"Lambri","year":"2023","journal-title":"Phys Med"},{"key":"10.1016\/j.meddos.2025.07.006_bib0027","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1002\/mp.16210","article-title":"A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network","volume":"50","author":"Matsuura","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0028","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14055","article-title":"Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution","volume":"24","author":"Tozuka","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0029","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1002\/mp.16091","article-title":"Patient-specific quality assurance prediction models based on machine learning for novel dual-layered MLC linac","volume":"50","author":"Zhu","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0030","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14215","article-title":"Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution","volume":"25","author":"Ishizaka","year":"2024","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0031","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20190270","article-title":"Complexity metrics for IMRT and VMAT plans: a review of current literature and applications","volume":"92","author":"Chiavassa","year":"2019","journal-title":"Br J Radiol"},{"issue":"8","key":"10.1016\/j.meddos.2025.07.006_bib0032","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/S1470-2045(16)30070-5","article-title":"Hypofractionated versus conventionally fractionated radiotherapy for patients with localised prostate cancer (HYPRO): final efficacy results from a randomised, multicentre, open-label, phase 3 trial","volume":"17","author":"Incrocci","year":"2016","journal-title":"Lancet Oncol"},{"issue":"5","key":"10.1016\/j.meddos.2025.07.006_bib0033","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1016\/j.ijrobp.2005.05.054","article-title":"Hypofractionated intensity-modulated radiotherapy (70 Gy at 2.5 Gy per fraction) for localized prostate cancer: long-term outcomes","volume":"63","author":"Kupelian","year":"2005","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"2","key":"10.1016\/j.meddos.2025.07.006_bib0034","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.radonc.2013.08.030","article-title":"Stereotactic body radiotherapy for localized prostate cancer: pooled analysis from a multi-institutional consortium of prospective phase II trials","volume":"109","author":"King","year":"2013","journal-title":"Radiother Oncol"},{"issue":"5","key":"10.1016\/j.meddos.2025.07.006_bib0035","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1093\/jrr\/rrab063","article-title":"Feasibility of intensity modulated radiotherapy with involved field radiotherapy for Japanese patients with locally advanced non-small cell lung cancer","volume":"62","author":"Abe","year":"2021","journal-title":"J Radiat Res"},{"key":"10.1016\/j.meddos.2025.07.006_bib0036","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1186\/1748-717X-5-81","article-title":"Clinical outcomes of stereotactic body radiotherapy for stage I non-small cell lung cancer using different doses depending on tumor size","volume":"5","author":"Baba","year":"2010","journal-title":"Radiat Oncol"},{"issue":"2","key":"10.1016\/j.meddos.2025.07.006_bib0037","article-title":"Ten-fraction stereotactic radiosurgery with different gross tumor doses and inhomogeneities for brain metastasis of >10 cc: treatment responses suggesting suitable biological effective dose formula for single and 10 fractions","volume":"15","author":"Ohtakara","year":"2023","journal-title":"Cureus"},{"issue":"6","key":"10.1016\/j.meddos.2025.07.006_bib0038","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1001\/jamaoncol.2023.0356","article-title":"Stereotactic radiosurgery vs conventional radiotherapy for localized vertebral metastases of the spine: phase 3 results of NRG oncology\/RTOG 0631 randomized clinical trial","volume":"9","author":"Ryu","year":"2023","journal-title":"JAMA Oncol"},{"key":"10.1016\/j.meddos.2025.07.006_bib0039","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1007\/s10147-020-01665-2","article-title":"A phase II study of adaptive two-step intensity-modulated radiation therapy (IMRT) with chemotherapy for loco-regionally advanced nasopharyngeal cancer (JCOG1015)","volume":"25","author":"Nishimura","year":"2020","journal-title":"Int J Clin Oncol"},{"key":"10.1016\/j.meddos.2025.07.006_bib0040","doi-asserted-by":"crossref","first-page":"12777","DOI":"10.1007\/s11042-019-08453-9","article-title":"Dropout vs. batch normalization: an empirical study of their impact to deep learning","volume":"79","author":"Garbin","year":"2020","journal-title":"Multimedia Tools Appl"},{"key":"10.1016\/j.meddos.2025.07.006_bib0041","doi-asserted-by":"crossref","first-page":"7415","DOI":"10.1002\/mp.17298","article-title":"Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability","volume":"51","author":"Cho","year":"2024","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2025.07.006_bib0042","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.radonc.2015.11.034","article-title":"A tool to include gamma analysis software into a quality assurance program","volume":"118","author":"Agnew","year":"2016","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.meddos.2025.07.006_bib0043","unstructured":"Lundberg S. A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874. 2017. doi:10.48550\/arXiv.1705.07874."},{"key":"10.1016\/j.meddos.2025.07.006_bib0044","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int J Comput Vis"}],"container-title":["Medical Dosimetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0958394725000470?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0958394725000470?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T10:33:34Z","timestamp":1769596414000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0958394725000470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026]]}},"alternative-id":["S0958394725000470"],"URL":"https:\/\/doi.org\/10.1016\/j.meddos.2025.07.006","relation":{},"ISSN":["0958-3947"],"issn-type":[{"value":"0958-3947","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Convolutional neural network-based prediction of gamma pass rate in volumetric modulated arc therapy using 3d fluence maps and complexity metrics","name":"articletitle","label":"Article Title"},{"value":"Medical Dosimetry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.meddos.2025.07.006","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}