{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:30:44Z","timestamp":1772166644090,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"PRIMAGE","award":["826494"],"award-info":[{"award-number":["826494"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01086-w","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T14:32:31Z","timestamp":1739975551000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic magnetic resonance imaging series labelling for large repositories"],"prefix":"10.1186","volume":"12","author":[{"given":"Armando","family":"Gomis-Maya","sequence":"first","affiliation":[]},{"given":"Leonor","family":"Cerd\u00e1-Alberich","sequence":"additional","affiliation":[]},{"given":"Diana","family":"Veiga-Canuto","sequence":"additional","affiliation":[]},{"given":"Salvatore","family":"Claudio-Fanni","sequence":"additional","affiliation":[]},{"given":"Amadeo","family":"Ten-Steve","sequence":"additional","affiliation":[]},{"given":"Gloria","family":"Ribas-Despuig","sequence":"additional","affiliation":[]},{"given":"Pedro Jos\u00e9","family":"Mallol-Rosell\u00f3","sequence":"additional","affiliation":[]},{"given":"Joan","family":"Vila-Frances","sequence":"additional","affiliation":[]},{"given":"Luis","family":"Marti-Bonmati","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"issue":"12","key":"1086_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12123858","volume":"12","author":"L Cerd\u00e1 Alberich","year":"2020","unstructured":"Cerd\u00e1 Alberich L, et al. A confidence habitats methodology in MR quantitative diffusion for the classification of neuroblastic tumors. Cancers. 2020;12(12): 12. https:\/\/doi.org\/10.3390\/cancers12123858.","journal-title":"Cancers"},{"issue":"3","key":"1086_CR2","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1002\/jmri.27625","volume":"54","author":"A Rodr\u00edguez-Ortega","year":"2021","unstructured":"Rodr\u00edguez-Ortega A, et al. Machine learning-based integration of prognostic magnetic resonance imaging biomarkers for myometrial invasion stratification in endometrial cancer. J Magn Reson Imaging. 2021;54(3):987\u201395. https:\/\/doi.org\/10.1002\/jmri.27625.","journal-title":"J Magn Reson Imaging"},{"key":"1086_CR3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s40644-020-00329-8","volume":"20","author":"Y Suter","year":"2020","unstructured":"Suter Y. Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques. Cancer Imaging. 2020;20:13.","journal-title":"Cancer Imaging"},{"issue":"10","key":"1086_CR4","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1007\/s11547-021-01389-x","volume":"126","author":"C Scapicchio","year":"2021","unstructured":"Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. Radiol Med. 2021;126(10):1296\u2013311. https:\/\/doi.org\/10.1007\/s11547-021-01389-x.","journal-title":"Radiol Med"},{"issue":"1","key":"1086_CR5","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s41747-020-00150-9","volume":"4","author":"L Mart\u00ed-Bonmat\u00ed","year":"2020","unstructured":"Mart\u00ed-Bonmat\u00ed L, et al. PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur Radiol Exp. 2020;4(1):22. https:\/\/doi.org\/10.1186\/s41747-020-00150-9.","journal-title":"Eur Radiol Exp"},{"key":"1086_CR6","first-page":"11","volume":"12","author":"L Mart","year":"2022","unstructured":"Mart L. CHAIMELEON project: creation of a pan-European Repository of health imaging data for the development of AI-powered cancer management tools. Front Oncol. 2022;12:11.","journal-title":"Front Oncol"},{"key":"1086_CR7","unstructured":"An AI Platform integrating imaging data and models, supporting precision care through prostate cancer\u2019s continuum | ProCAncer-I Project | Fact Sheet | H2020\u2019, CORDIS | European Commission. https:\/\/cordis.europa.eu\/project\/id\/952159 Accessed 6 Sep 2022."},{"key":"1086_CR8","unstructured":"A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer | INCISIVE Project | Fact Sheet | H2020 | CORDIS | European Commission. https:\/\/cordis.europa.eu\/project\/id\/952179 Accessed 6 Sep 2022."},{"key":"1086_CR9","unstructured":"A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology | EuCanImage Project | Fact Sheet | H2020 | CORDIS | European Commission. https:\/\/cordis.europa.eu\/project\/id\/952103\/es. Accessed 11 Oct 2022."},{"key":"1086_CR10","doi-asserted-by":"publisher","unstructured":"Tanwar M, Duggal R, Khatri SK. Unravelling unstructured data: A wealth of information in big data. In: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions). 2015. pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICRITO.2015.7359270.","DOI":"10.1109\/ICRITO.2015.7359270"},{"key":"1086_CR11","doi-asserted-by":"publisher","first-page":"46","DOI":"10.14445\/22312803\/IJCTT-V38P109","volume":"38","author":"A Eberendu","year":"2016","unstructured":"Eberendu A. Unstructured data: an overview of the data of big data. Int J Comput Trends Technol. 2016;38:46\u201350. https:\/\/doi.org\/10.14445\/22312803\/IJCTT-V38P109.","journal-title":"Int J Comput Trends Technol"},{"issue":"2","key":"1086_CR12","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s10278-019-00282-4","volume":"33","author":"S Ranjbar","year":"2020","unstructured":"Ranjbar S, et al. A deep convolutional neural network for annotation of magnetic resonance imaging sequence type. J Digit Imaging. 2020;33(2):439\u201346. https:\/\/doi.org\/10.1007\/s10278-019-00282-4.","journal-title":"J Digit Imaging"},{"issue":"1","key":"1086_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s12021-018-9387-8","volume":"17","author":"R Pizarro","year":"2019","unstructured":"Pizarro R, et al. using deep learning algorithms to automatically identify the brain MRI contrast: implications for managing large databases. Neuroinformatics. 2019;17(1):115\u201330. https:\/\/doi.org\/10.1007\/s12021-018-9387-8.","journal-title":"Neuroinformatics"},{"key":"1086_CR14","doi-asserted-by":"publisher","unstructured":"de Mello JPV, et al. Deep learning-based type identification of volumetric MRI sequences. In: 2020 25th International Conference on Pattern Recognition (ICPR). 2021. pp. 1\u20138. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413120.","DOI":"10.1109\/ICPR48806.2021.9413120"},{"key":"1086_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2021.622951","author":"S Liang","year":"2021","unstructured":"Liang S, et al. Magnetic resonance imaging sequence identification using a metadata learning approach. Front Neuroinform. 2021. https:\/\/doi.org\/10.3389\/fninf.2021.622951.","journal-title":"Front Neuroinform"},{"issue":"3","key":"1086_CR16","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s10278-019-00308-x","volume":"33","author":"R Gauriau","year":"2020","unstructured":"Gauriau R, et al. Using DICOM metadata for radiological image series categorization: a feasibility study on large clinical brain MRI datasets. J Digit Imaging. 2020;33(3):747\u201362. https:\/\/doi.org\/10.1007\/s10278-019-00308-x.","journal-title":"J Digit Imaging"},{"key":"1086_CR17","unstructured":"Florea F, Rogozan A, Bensrhair A, Dacher J-N, Darmoni S. Modality categorization by textual annotations interpretation in medical imaging. 2022."},{"issue":"1","key":"1086_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14010070","volume":"14","author":"S Na","year":"2024","unstructured":"Na S, et al. Sequence-Type classification of brain MRI for acute stroke using a self-supervised machine learning algorithm. Diagnostics. 2024;14(1): 1. https:\/\/doi.org\/10.3390\/diagnostics14010070.","journal-title":"Diagnostics"},{"key":"1086_CR19","unstructured":"PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers | PRIMAGE Project | Fact Sheet | H2020 | CORDIS | European Commission. https:\/\/cordis.europa.eu\/project\/id\/826494. Accessed 6 Sep 2022."},{"key":"1086_CR20","unstructured":"MongoDB Atlas: Cloud Document Database. MongoDB. https:\/\/www.mongodb.com\/cloud\/atlas\/lp\/try4. Accessed 7 Sep 2022."},{"key":"1086_CR21","doi-asserted-by":"publisher","first-page":"102062","DOI":"10.1016\/j.media.2021.102062","volume":"71","author":"S Budd","year":"2021","unstructured":"Budd S, Robinson EC, Kainz B. A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med Image Anal. 2021;71:102062. https:\/\/doi.org\/10.1016\/j.media.2021.102062.","journal-title":"Med Image Anal"},{"key":"1086_CR22","doi-asserted-by":"publisher","first-page":"271","DOI":"10.3233\/FI-2010-288","volume":"101","author":"M Kursa","year":"2010","unstructured":"Kursa M, Jankowski A, Rudnicki W. Boruta\u2014a system for feature selection. Fundam Inf. 2010;101:271\u201385. https:\/\/doi.org\/10.3233\/FI-2010-288.","journal-title":"Fundam Inf"},{"issue":"1","key":"1086_CR23","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324.","journal-title":"Mach Learn"},{"key":"1086_CR24","doi-asserted-by":"publisher","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. CatBoost: unbiased boosting with categorical features. 2019. arXiv: arXiv:1706.09516. https:\/\/doi.org\/10.48550\/arXiv.1706.09516.","DOI":"10.48550\/arXiv.1706.09516"},{"key":"1086_CR25","unstructured":"Batista G, Monard M-C. A study of K-nearest neighbour as an imputation method. 2002;30:260."},{"key":"1086_CR26","doi-asserted-by":"publisher","DOI":"10.1148\/rg.296095516","author":"SM Erturk","year":"2009","unstructured":"Erturk SM, Alberich-Bayarri A, Herrmann KA, Marti-Bonmati L, Ros PR. Use of 3.0-T MR imaging for evaluation of the abdomen. Radiographics. 2009. https:\/\/doi.org\/10.1148\/rg.296095516.","journal-title":"Radiographics"},{"key":"1086_CR27","unstructured":"Skalski M. MRI sequence parameters | Radiology Reference Article | Radiopaedia.org\u2019. Radiopaedia. https:\/\/radiopaedia.org\/articles\/mri-sequence-parameters. Accessed 11 Oct 2022."},{"issue":"1","key":"1086_CR28","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/s40537-019-0217-0","volume":"6","author":"S Dash","year":"2019","unstructured":"Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data. 2019;6(1):54. https:\/\/doi.org\/10.1186\/s40537-019-0217-0.","journal-title":"J Big Data"},{"issue":"14","key":"1086_CR29","doi-asserted-by":"publisher","first-page":"3449","DOI":"10.3390\/cancers13143449","volume":"13","author":"M Carles","year":"2021","unstructured":"Carles M, et al. 18F-FMISO-PET hypoxia monitoring for head-and-neck cancer patients: radiomics analyses predict the outcome of chemo-radiotherapy. Cancers. 2021;13(14):3449. https:\/\/doi.org\/10.3390\/cancers13143449.","journal-title":"Cancers"},{"issue":"1137","key":"1086_CR30","doi-asserted-by":"publisher","first-page":"20220072","DOI":"10.1259\/bjr.20220072","volume":"95","author":"L Marti-Bonmati","year":"2022","unstructured":"Marti-Bonmati L, et al. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol. 2022;95(1137):20220072. https:\/\/doi.org\/10.1259\/bjr.20220072.","journal-title":"Br J Radiol"},{"key":"1086_CR31","unstructured":"EUCAIM. Building a Data-Driven Future for Cancer Care. Valencia, Spain. Available from https:\/\/cancerimage.eu\/. Accessed 28 June 2023."},{"issue":"1","key":"1086_CR32","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock JT, Khoshgoftaar TM. CatBoost for big data: an interdisciplinary review. J Big Data. 2020;7(1):94. https:\/\/doi.org\/10.1186\/s40537-020-00369-8.","journal-title":"J Big Data"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01086-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01086-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01086-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T14:32:38Z","timestamp":1739975558000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01086-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,19]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1086"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01086-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4369514\/v1","asserted-by":"object"}]},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,19]]},"assertion":[{"value":"4 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study has been approved by the Hospital\u2019s Ethics Committee (The Ethics Committee for Investigation with medicinal products of the University and Polytechnic La Fe Hospital, ethic code: 2018\/0228).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39"}}