{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T02:58:48Z","timestamp":1791255528352,"version":"4.1.0"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","physicamedica.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Physica Medica"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.ejmp.2025.105065","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T06:57:07Z","timestamp":1754549827000},"page":"105065","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Robustness evaluation of an artificial intelligence-based automatic contouring software in daily routine practice"],"prefix":"10.1016","volume":"137","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5339-5868","authenticated-orcid":false,"given":"Jimmy","family":"Fontaine","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maud","family":"Suszko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5478-0565","authenticated-orcid":false,"given":"Francesca","family":"di Franco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agathe","family":"Leroux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emilie","family":"Bonnet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathieu","family":"Bosset","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Langrand-Escure","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e9bastien","family":"Clippe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0747-7915","authenticated-orcid":false,"given":"Bertrand","family":"Fleury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Baptiste","family":"Guy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejmp.2025.105065_b0005","doi-asserted-by":"crossref","first-page":"136","DOI":"10.4103\/0971-6203.44472","article-title":"Tumor delineation: The weakest link in the search for accuracy in radiotherapy","volume":"33","author":"Njeh","year":"2008","journal-title":"J Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0010","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0167-8140(98)00021-8","article-title":"Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning","volume":"47","author":"Fiorino","year":"1998","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ejmp.2025.105065_b0015","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.ijrobp.2010.10.019","article-title":"Variations in the Contouring of Organs at Risk: Test Case From a Patient With Oropharyngeal Cancer","volume":"82","author":"Nelms","year":"2012","journal-title":"International Journal of Radiation Oncology Biology Physics"},{"key":"10.1016\/j.ejmp.2025.105065_b0020","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1016\/j.ijrobp.2008.10.034","article-title":"Variability of Target and Normal Structure Delineation for Breast Cancer Radiotherapy: An RTOG Multi-Institutional and Multiobserver Study","volume":"73","author":"Li","year":"2009","journal-title":"International Journal of Radiation Oncology Biology Physics"},{"key":"10.1016\/j.ejmp.2025.105065_b0025","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.canrad.2021.06.023","article-title":"Clinical implementation of deep-learning based auto-contouring tools\u2013Experience of three French radiotherapy centers","volume":"25","author":"Robert","year":"2021","journal-title":"Cancer\/Radioth\u00e9rapie"},{"key":"10.1016\/j.ejmp.2025.105065_b0030","article-title":"Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives","author":"Erdur","year":"2024","journal-title":"Strahlenther Onkol"},{"key":"10.1016\/j.ejmp.2025.105065_b0035","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1002\/jmrs.729","article-title":"Automation and artificial intelligence in radiation therapy treatment planning","volume":"71","author":"Jones","year":"2024","journal-title":"J of Medical Radiation Sci"},{"key":"10.1016\/j.ejmp.2025.105065_b0040","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.3390\/cancers16142538","article-title":"Segmentation-Free Outcome Prediction from Head and Neck Cancer PET\/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)","volume":"16","author":"Toosi","year":"2024","journal-title":"Cancers"},{"key":"10.1016\/j.ejmp.2025.105065_b0045","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.3390\/life12122088","article-title":"Implementation of a Commercial Deep Learning-Based Auto Segmentation Software in Radiotherapy: Evaluation of Effectiveness and Impact on Workflow","volume":"12","author":"Radici","year":"2022","journal-title":"Life"},{"key":"10.1016\/j.ejmp.2025.105065_b0050","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.phro.2020.11.002","article-title":"Machine learning applications in radiation oncology: Current use and needs to support clinical implementation","volume":"16","author":"Brouwer","year":"2020","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.ejmp.2025.105065_b0055","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.radonc.2020.09.008","article-title":"Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance","volume":"153","author":"Vandewinckele","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ejmp.2025.105065_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2024.110345","article-title":"A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy","volume":"197","author":"Hurkmans","year":"2024","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ejmp.2025.105065_b0065","first-page":"kl 69","article-title":"\u00dcber die bestimmung von funktionen durch ihre integralwerte l\u00e4ngs gewisser mannigfaltigkeiten. Ber Verh S\u00e4chs Akad Wiss Leipzig","volume":"262 \u201377","author":"Radon","year":"1917","journal-title":"Math Nat."},{"key":"10.1016\/j.ejmp.2025.105065_b0070","unstructured":"Philips Healthcare. White Paper: iDose4 Iterative Reconstruction Technique: Breakthrough in Image Quality and Dose Reduction. Eindhoven, Pays-Bas : Koninklijke Philips N.V.; 2010. Code : 452299112681."},{"key":"10.1016\/j.ejmp.2025.105065_b0075","doi-asserted-by":"crossref","DOI":"10.1118\/1.4885976","article-title":"Characterization of a commercial hybrid iterative and model\u2010based reconstruction algorithm in radiation oncology","volume":"41","author":"Price","year":"2014","journal-title":"Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0080","unstructured":"Morton T. White Paper: Basic IMR Testing, Considerations, and Image Quality Trends. Eindhoven, Pays-Bas : Koninklijke Philips N.V.; 2015. Code : 4522 991 12681."},{"key":"10.1016\/j.ejmp.2025.105065_b0085","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the Amount of Ecologic Association Between Species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"10.1016\/j.ejmp.2025.105065_b0090","series-title":"Grundz\u00fcge der Mengenlehre","author":"Hausdorff","year":"1914"},{"key":"10.1016\/j.ejmp.2025.105065_b0095","first-page":"279","article-title":"Wilcoxon Statistics in R: Signed-Rank Test and Rank-Sum Test. R Programming","author":"Okoye","year":"2024","journal-title":"Singapore: Springer Nature Singapore"},{"key":"10.1016\/j.ejmp.2025.105065_b0100","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14461","article-title":"Geometric and dosimetric evaluation for breast and regional nodal auto\u2010segmentation structures","volume":"25","author":"Tsui","year":"2024","journal-title":"J Applied Clin Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0105","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s13014-024-02451-4","article-title":"Evaluation of multiple-vendor AI autocontouring solutions","volume":"19","author":"Goddard","year":"2024","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.ejmp.2025.105065_b0110","doi-asserted-by":"crossref","first-page":"5735","DOI":"10.3390\/cancers15245735","article-title":"Clinical Use of a Commercial Artificial Intelligence-Based Software for Autocontouring in Radiation Therapy: Geometric Performance and Dosimetric Impact","volume":"15","author":"Hoque","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.ejmp.2025.105065_b0115","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ac678a","article-title":"Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency","volume":"67","author":"Barrag\u00e1n-Montero","year":"2022","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.ejmp.2025.105065_b0120","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s10111-018-0525-8","article-title":"The \u201cOut-of-the-Loop\u201d concept in automated driving: proposed definition, measures and implications","volume":"21","author":"Merat","year":"2019","journal-title":"Cogn Tech Work"},{"key":"10.1016\/j.ejmp.2025.105065_b0125","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ejmp.2021.02.026","article-title":"Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance","volume":"83","author":"Barrag\u00e1n-Montero","year":"2021","journal-title":"Phys Med"},{"key":"10.1016\/j.ejmp.2025.105065_b0130","doi-asserted-by":"crossref","unstructured":"Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 2025:e081554. doi: 10.1136\/bmj-2024-081554.","DOI":"10.1136\/bmj-2024-081554"},{"key":"10.1016\/j.ejmp.2025.105065_b0135","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.ejmp.2021.04.006","article-title":"Procurement, commissioning and QA of AI based solutions: An MPE\u2019s perspective on introducing AI in clinical practice","volume":"83","author":"Bosmans","year":"2021","journal-title":"Phys Med"},{"key":"10.1016\/j.ejmp.2025.105065_b0140","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1002\/acm2.13207","article-title":"Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms","volume":"22","author":"Huang","year":"2021","journal-title":"J Applied Clin Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0145","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ejmp.2022.06.015","article-title":"The geometric and dosimetric effect of algorithm choice on propagated contours from CT to cone beam CTs","volume":"100","author":"Nash","year":"2022","journal-title":"Phys Med"},{"key":"10.1016\/j.ejmp.2025.105065_b0150","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13951","article-title":"Geometric and dosimetric evaluation of deep learning based auto\u2010segmentation for clinical target volume on breast cancer","volume":"24","author":"Zhong","year":"2023","journal-title":"J Applied Clin Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0155","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1016\/j.ijrobp.2018.11.048","article-title":"Clinically Oriented Contour Evaluation Using Dosimetric Indices Generated From Automated Knowledge-Based Planning","volume":"103","author":"Lim","year":"2019","journal-title":"International Journal of Radiation Oncology*Biology*Physics"},{"key":"10.1016\/j.ejmp.2025.105065_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102161","article-title":"The predictive value of segmentation metrics on dosimetry in organs at risk of the brain","volume":"73","author":"Poel","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.ejmp.2025.105065_b0165","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1120\/jacmp.v17i2.5889","article-title":"The suitability of common metrics for assessing parotid and larynx autosegmentation accuracy","volume":"17","author":"Beasley","year":"2016","journal-title":"J Applied Clin Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0170","doi-asserted-by":"crossref","first-page":"5027","DOI":"10.1118\/1.4927567","article-title":"Validation of clinical acceptability of an atlas\u2010based segmentation algorithm for the delineation of organs at risk in head and neck cancer","volume":"42","author":"Hoang Duc","year":"2015","journal-title":"Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0175","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.ijrobp.2019.02.040","article-title":"Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation","volume":"104","author":"Van Rooij","year":"2019","journal-title":"International Journal of Radiation Oncology*Biology*Physics"},{"key":"10.1016\/j.ejmp.2025.105065_b0180","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.phro.2019.12.001","article-title":"Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy","volume":"13","author":"Vaassen","year":"2020","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.ejmp.2025.105065_b0185","doi-asserted-by":"crossref","DOI":"10.2196\/26151","article-title":"Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study","volume":"23","author":"Nikolov","year":"2021","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.ejmp.2025.105065_b0190","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.radonc.2021.05.003","article-title":"Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review","volume":"160","author":"Sherer","year":"2021","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ejmp.2025.105065_b0195","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.1002\/mp.16299","article-title":"Contouring quality assurance methodology based on multiple geometric features against deep learning auto\u2010segmentation","volume":"50","author":"Duan","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.ejmp.2025.105065_b0200","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.ejmp.2025.105065_b0205","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1002\/mp.17444","article-title":"Development, validation, and application of a generic image\u2010based noise addition method for simulating reduced dose computed tomography images","volume":"52","author":"Alsaihati","year":"2025","journal-title":"Med Phys"}],"container-title":["Physica Medica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1120179725001759?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1120179725001759?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T22:53:36Z","timestamp":1762815216000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1120179725001759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":41,"alternative-id":["S1120179725001759"],"URL":"https:\/\/doi.org\/10.1016\/j.ejmp.2025.105065","relation":{},"ISSN":["1120-1797"],"issn-type":[{"value":"1120-1797","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Robustness evaluation of an artificial intelligence-based automatic contouring software in daily routine practice","name":"articletitle","label":"Article Title"},{"value":"Physica Medica","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejmp.2025.105065","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"105065"}}