{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:00:42Z","timestamp":1784818842320,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload. Methods. Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance. Results. For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%. Conclusions. The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.<\/jats:p>","DOI":"10.3390\/diagnostics13050943","type":"journal-article","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T03:54:56Z","timestamp":1677729296000},"page":"943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine"],"prefix":"10.3390","volume":"13","author":[{"given":"No\u00e9mie","family":"Moreau","sequence":"first","affiliation":[{"name":"Medical Physics Department, CLCC Fran\u00e7ois Baclesse, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurine","family":"Bonnor","sequence":"additional","affiliation":[{"name":"Medical Physics Department, CLCC Fran\u00e7ois Baclesse, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cyril","family":"Jaudet","sequence":"additional","affiliation":[{"name":"Medical Physics Department, CLCC Fran\u00e7ois Baclesse, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laetitia","family":"Lechippey","sequence":"additional","affiliation":[{"name":"Medical Physics Department, CLCC Fran\u00e7ois Baclesse, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia","family":"Falzone","sequence":"additional","affiliation":[{"name":"GenesisCare Theranostics, Building 1 & 11, The Mill, 41\u201343 Bourke Road, Alexandria, NSW 2015, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alain","family":"Batalla","sequence":"additional","affiliation":[{"name":"Medical Physics Department, CLCC Fran\u00e7ois Baclesse, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cindy","family":"Bertaut","sequence":"additional","affiliation":[{"name":"Medical Physics Department, Cherbourg Hospital, 50100 Cherbourg, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9826-6565","authenticated-orcid":false,"given":"Aur\u00e9lien","family":"Corroyer-Dulmont","sequence":"additional","affiliation":[{"name":"Medical Physics Department, CLCC Fran\u00e7ois Baclesse, 14000 Caen, France"},{"name":"Normandie University, UNICAEN, CNRS, ISTCT, CYCERON, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,2]]},"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":"2007","journal-title":"Med. Phys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ejmp.2019.05.024","article-title":"Use of metrics to quantify IMRT and VMAT treatment plan complexity: A systematic review and perspectives","volume":"64","author":"Antoine","year":"2019","journal-title":"Phys. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1118\/1.598248","article-title":"A technique for the quantitative evaluation of dose distributions","volume":"25","author":"Low","year":"1998","journal-title":"Med. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.1002\/mp.13526","article-title":"Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches","volume":"46","author":"Ge","year":"2019","journal-title":"Med. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20190270","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."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shen, C., Chen, L., Zhong, X., Gonzalez, Y., Visak, J., Meng, B., Inam, E., Parsons, D., Godley, A., and Jiang, S. (2023). Clinical experience on patient-specific quality assurance for CBCT-based online adaptive treatment plan. J. Appl. Clin. Med. Phys., e13918.","DOI":"10.1002\/acm2.13918"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e200075","DOI":"10.1148\/rycan.2021200075","article-title":"A Framework for Sharing Radiation Dose Distribution Maps in the Electronic Medical Record for Improving Multidisciplinary Patient Management","volume":"3","author":"Savjani","year":"2021","journal-title":"Radiol. Imaging Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sadowski, B., Milewska, K., and Ginter, J. (2022). Machine Learning Based Prediction of Gamma Passing Rate for VMAT Radiotherapy Plans. J. Pers. Med., 12.","DOI":"10.3390\/jpm12122071"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"S87","DOI":"10.1016\/S1120-1797(22)00184-3","article-title":"Patient-QA prediction: A new approach of complexity indexes","volume":"92","author":"Jazouli","year":"2021","journal-title":"Phys. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kubben, P., Dumontier, M., and Dekker, A. (2019). Fundamentals of Clinical Data Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-99713-1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"577620","DOI":"10.3389\/frai.2020.577620","article-title":"Integration of AI and Machine Learning in Radiotherapy QA","volume":"3","author":"Chan","year":"2020","journal-title":"Front. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"095017","DOI":"10.1088\/1361-6560\/ab142e","article-title":"Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics","volume":"64","author":"Granville","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"ref_13","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":"ref_14","doi-asserted-by":"crossref","first-page":"235023","DOI":"10.1088\/1361-6560\/abb31c","article-title":"Multi-task autoencoder based classification-regression model for patient-specific VMAT QA","volume":"65","author":"Wang","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104816","DOI":"10.1016\/j.compbiomed.2021.104816","article-title":"COVID-19 detection in chest X-ray images using deep boosted hybrid learning","volume":"137","author":"Khan","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_16","unstructured":"Corroyer-Dulmont, A. (2023, February 27). DeepHybridLearning_RadiotherapyQA. Available online: https:\/\/github.com\/AurelienCD\/DeepHybridLearning_RadiotherapyQA_Depository_ManuscriptID_22-1397."},{"key":"ref_17","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","unstructured":"Chollet, F. (2023, February 27). Keras. Available online: https:\/\/docs.anaconda.com."},{"key":"ref_19","first-page":"559","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Lemaitre","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","unstructured":"(2020). Anaconda Software Distribution, Anaconda Inc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"seaborn: Statistical data visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1120\/jacmp.v4i1.2540","article-title":"Patient specific quality assurance for the delivery of intensity modulated radiotherapy","volume":"4","author":"Agazaryan","year":"2003","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_24","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":"ref_25","first-page":"623","article-title":"Artificial intelligence for quality assurance in radiotherapy","volume":"25","author":"Simon","year":"2021","journal-title":"Cancer"},{"key":"ref_26","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. Med."},{"key":"ref_27","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":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ejmp.2017.10.074","article-title":"49. Current uses of log files in the radiotherapy quality assurance workflow for IMRT and VMAT techniques","volume":"44","author":"Legrand","year":"2017","journal-title":"Phys. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1120\/jacmp.v16i3.5283","article-title":"Anatomy-based, patient-specific VMAT QA using EPID or MLC log files","volume":"16","author":"Defoor","year":"2015","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1002\/acm2.13053","article-title":"Patient-specific quality assurance using machine log files analysis for stereotactic body radiation therapy (SBRT)","volume":"21","author":"Chow","year":"2020","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.radonc.2021.06.024","article-title":"Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario","volume":"161","author":"Yang","year":"2021","journal-title":"Radiother. Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.jacr.2022.03.015","article-title":"Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems","volume":"19","author":"Darzidehkalani","year":"2022","journal-title":"J. Am. Coll. Radiol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/5\/943\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:45:50Z","timestamp":1760121950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/5\/943"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,2]]},"references-count":33,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["diagnostics13050943"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13050943","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,2]]}}}