{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:19:29Z","timestamp":1785435569403,"version":"3.56.0"},"reference-count":22,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2023-00253604"],"award-info":[{"award-number":["RS-2023-00253604"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"award":["RS-2023-00253604"],"award-info":[{"award-number":["RS-2023-00253604"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade Industry & Energy","doi-asserted-by":"publisher","award":["RS-2025-02221011"],"award-info":[{"award-number":["RS-2025-02221011"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013129","name":"Ministry of SMEs and Startups","doi-asserted-by":"crossref","award":["RS-2025-25439348"],"award-info":[{"award-number":["RS-2025-25439348"]}],"id":[{"id":"10.13039\/501100013129","id-type":"DOI","asserted-by":"crossref"}]},{"award":["RS-2025-25439348"],"award-info":[{"award-number":["RS-2025-25439348"]}],"id":[{"id":"https:\/\/ror.org\/022c4zk48","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Multi-leaf collimator (MLC) positioning accuracy critically affects delivered dose fidelity in volumetric modulated arc therapy (VMAT), yet conventional gamma-based quality assurance (QA) provides only plan-level pass\/fail outcomes without leaf-specific error localization. This study developed and validated a convolutional neural network (CNN) framework that classifies the magnitude and direction of individual MLC leaf positioning errors directly from fluence map data. Methods: Three patient cohorts were analyzed: 20 prostate cancer patients for model development under an 8:1:1 train\/validation\/test split and 20 additional prostate and 10 head and neck (H&amp;N) patients reserved for external validation. For inner MLC leaves 21\u201340, systematic offsets from \u22125 mm to +5 mm in 1.0 mm increments were independently applied to the two leaf banks, yielding 121 error combinations per leaf. A CNN was trained as a 121-class classifier on two-channel inputs pairing the reference and error-induced fluence map regions and was compared against three tree-based baselines using five-fold cross-validation. Results: The CNN achieved 97.00% accuracy on the internal test set and 96.54 \u00b1 0.43% accuracy across the five patient-level cross-validation folds. Across all test samples, 99.88% and 99.83% of predictions were within 1 mm of the true offset for Bank A and Bank B, respectively, well within the AAPM TG-142 1 mm MLC positioning tolerance. External validation yielded 96.19% accuracy on the additional prostate cohort and 93.72% on the H&amp;N cohort, suggesting reproducibility within the same anatomical site and potential robustness across anatomically distinct treatment sites within a single-institution dataset. Conclusions: The proposed CNN framework demonstrates the feasibility of leaf-specific identification of MLC positioning errors in both magnitude and direction from simulated fluence maps. These findings support further investigation using physically measured fluence data for future clinical translation.<\/jats:p>","DOI":"10.3390\/jcm15135136","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T16:50:33Z","timestamp":1782924633000},"page":"5136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leaf-Specific Classification of Multi-Leaf Collimator Positioning Errors in Volumetric Modulated Arc Therapy Using a Convolutional Neural Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5089-1491","authenticated-orcid":false,"given":"Ju Yeol","family":"Shin","sequence":"first","affiliation":[{"name":"Paprica Lab. Co., Ltd., Seoul 03123, Republic of Korea"},{"name":"Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang Heon","family":"Choi","sequence":"additional","affiliation":[{"name":"Paprica Lab. Co., Ltd., Seoul 03123, Republic of Korea"},{"name":"Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0305-5969","authenticated-orcid":false,"given":"Jung-in","family":"Kim","sequence":"additional","affiliation":[{"name":"Paprica Lab. Co., Ltd., Seoul 03123, Republic of Korea"},{"name":"Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Min","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5721-3347","authenticated-orcid":false,"given":"Wonjoong","family":"Cheon","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5643-1457","authenticated-orcid":false,"given":"So-Yeon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Veterans Health Service Medical Center, Seoul 05368, Republic of Korea"},{"name":"Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1118\/1.2818738","article-title":"Volumetric modulated arc therapy: IMRT in a single gantry arc","volume":"35","author":"Otto","year":"2008","journal-title":"Med. Phys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/0167-8140(88)90167-3","article-title":"Optimization of stationary and moving beam radiation therapy techniques","volume":"12","author":"Brahme","year":"1988","journal-title":"Radiother. Oncol."},{"key":"ref_3","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."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"031716","DOI":"10.1118\/1.4788645","article-title":"Patient QA systems for rotational radiation therapy: A comparative experimental study with intentional errors","volume":"40","author":"Fredh","year":"2013","journal-title":"Med. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.ejmp.2015.01.014","article-title":"The effect of beam interruption during VMAT delivery on the delivered dose distribution","volume":"31","author":"Heo","year":"2015","journal-title":"Phys. Medica"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7101","DOI":"10.1088\/0031-9155\/60\/18\/7101","article-title":"Modulation index for VMAT considering both mechanical and dose calculation uncertainties","volume":"60","author":"Park","year":"2015","journal-title":"Phys. Med. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ejmp.2019.02.016","article-title":"Evaluation of the plan delivery accuracy of intensity-modulated radiation therapy by texture analysis using fluence maps","volume":"59","author":"Park","year":"2019","journal-title":"Phys. Medica"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"031702","DOI":"10.1118\/1.4789580","article-title":"On the sensitivity of common gamma-index evaluation methods to MLC misalignments in Rapidarc quality assurance","volume":"40","author":"Heilemann","year":"2013","journal-title":"Med. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1186\/1748-717X-9-167","article-title":"The sensitivity of gamma-index method to the positioning errors of high-definition MLC in patient-specific VMAT QA for SBRT","volume":"9","author":"Kim","year":"2014","journal-title":"Radiat. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e53","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."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1120\/jacmp.v10i1.2915","article-title":"On the sensitivity of patient-specific IMRT QA to MLC positioning errors","volume":"10","author":"Yan","year":"2009","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7315","DOI":"10.1088\/0031-9155\/59\/23\/7315","article-title":"Modulation indices for volumetric modulated arc therapy","volume":"59","author":"Park","year":"2014","journal-title":"Phys. Med. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1016\/j.ejmp.2015.07.143","article-title":"Textural feature calculated from segmental fluences as a modulation index for VMAT","volume":"31","author":"Park","year":"2015","journal-title":"Phys. Medica"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s13014-018-1055-5","article-title":"To propose adding index of achievement (IOA) to IMRT QA process","volume":"13","author":"Kim","year":"2018","journal-title":"Radiat. Oncol."},{"key":"ref_15","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":"ref_16","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ijrobp.2018.05.033","article-title":"Error Detection in Intensity-Modulated Radiation Therapy Quality Assurance Using Radiomic Analysis of Gamma Distributions","volume":"102","author":"Wootton","year":"2018","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_17","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"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e14136","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":"ref_19","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1088\/0031-9155\/61\/6\/2514","article-title":"A machine learning approach to the accurate prediction of multi-leaf collimator positional errors","volume":"61","author":"Carlson","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1002\/mp.14014","article-title":"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":"ref_21","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1002\/mp.14670","article-title":"A tool for patient-specific prediction of delivery discrepancies in machine parameters using trajectory log files","volume":"48","author":"Chuang","year":"2021","journal-title":"Med. Phys."},{"key":"ref_22","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."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/13\/5136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:52:33Z","timestamp":1783054353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/13\/5136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,1]]},"references-count":22,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["jcm15135136"],"URL":"https:\/\/doi.org\/10.3390\/jcm15135136","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,1]]}}}