{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:29:48Z","timestamp":1785306588863,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"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>Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.<\/jats:p>","DOI":"10.3390\/diagnostics13152587","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T11:13:06Z","timestamp":1691061186000},"page":"2587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Radiomics for the Detection of Active Sacroiliitis Using MR Imaging"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0159-5424","authenticated-orcid":false,"given":"Matthaios","family":"Triantafyllou","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece"},{"name":"Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71500 Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2731-933X","authenticated-orcid":false,"given":"Michail E.","family":"Klontzas","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece"},{"name":"Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71500 Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2964-315X","authenticated-orcid":false,"given":"Emmanouil","family":"Koltsakis","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece"},{"name":"Department of Radiology, Karolinska University Hospital, 17164 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasiliki","family":"Papakosta","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Spanakis","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0927-2403","authenticated-orcid":false,"given":"Apostolos H.","family":"Karantanas","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece"},{"name":"Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71500 Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1136\/ard.2009.108233","article-title":"The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): Validation and final selection","volume":"68","author":"Rudwaleit","year":"2009","journal-title":"Ann. Rheum. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/art.1780270401","article-title":"Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria","volume":"27","author":"Cats","year":"1984","journal-title":"Arthritis Rheum"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106","DOI":"10.3389\/fmed.2018.00106","article-title":"The role of imaging in diagnosing axial spondyloarthritis","volume":"5","author":"Khmelinskii","year":"2018","journal-title":"Front. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e93","DOI":"10.5114\/pjr.2022.113390","article-title":"Magnetic resonance imaging of rheumatological diseases","volume":"87","author":"Weaver","year":"2022","journal-title":"Pol. J. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1007\/s00256-022-04018-4","article-title":"MRI in axial spondyloarthritis: Understanding an \u2018ASAS-positive MRI\u2019 and the ASAS classification criteria","volume":"51","author":"Diekhoff","year":"2022","journal-title":"Skelet. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3389\/fimmu.2020.00936","article-title":"Spine and Sacroiliac Joints Lesions on Magnetic Resonance Imaging in Early Axial-Spondyloarthritis During 24-Months Follow-Up (Italian Arm of SPACE Study)","volume":"11","author":"Lorenzin","year":"2020","journal-title":"Front. Immunol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1148\/radiol.212526","article-title":"Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints","volume":"305","author":"Bressem","year":"2022","journal-title":"Radiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1136\/annrheumdis-2016-210770","article-title":"2016 update of the ASAS-EULAR management recommendations for axial spondyloarthritis","volume":"76","author":"Ramiro","year":"2017","journal-title":"Ann. Rheum. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1227\/NEU.0000000000000988","article-title":"Randomized controlled trial of minimally invasive sacroiliac joint fusion using triangular titanium implants vs nonsurgical management for sacroiliac joint dysfunction: 12-month outcomes","volume":"77","author":"Polly","year":"2015","journal-title":"Neurosurgery"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_11","unstructured":"(2023, July 27). Biorender. Available online: https:\/\/www.biorender.com."},{"key":"ref_12","unstructured":"(2023, July 27). 3D Slicer. Available online: https:\/\/www.slicer.org."},{"key":"ref_13","unstructured":"(2023, July 27). Slicer Radiomics. Available online: https:\/\/www.radiomics.io\/slicerradiomics.html."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s40644-020-00311-4","article-title":"How to develop a meaningful radiomic signature for clinical use in oncologic patients","volume":"20","author":"Papanikolaou","year":"2020","journal-title":"Cancer Imaging"},{"key":"ref_15","unstructured":"(2023, May 27). Standard Scaler. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.StandardScaler.html."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jbspin.2015.08.013","article-title":"Sacroiliac joint edema by MRI: Far more often mechanical than inflammatory?","volume":"83","author":"Berthelot","year":"2016","journal-title":"Jt. Bone Spine"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109755","DOI":"10.1016\/j.ejrad.2021.109755","article-title":"Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis","volume":"140","author":"Kepp","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LLS.2016.2615086","article-title":"A Multimetric Evaluation of Stratified Random Sampling for Classification: A Case Study","volume":"2","author":"Thakur","year":"2016","journal-title":"IEEE Life Sci. Lett."},{"key":"ref_19","unstructured":"(2023, May 25). Boruta. Available online: https:\/\/pypi.org\/project\/Boruta\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1093\/bib\/bbx124","article-title":"Evaluation of variable selection methods for random forests and omics data sets","volume":"20","author":"Degenhardt","year":"2019","journal-title":"Brief. Bioinform."},{"key":"ref_21","unstructured":"(2023, July 23). Python. Available online: https:\/\/docs.python.org\/3\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s42358-020-00126-8","article-title":"Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging","volume":"60","author":"Faleiros","year":"2020","journal-title":"Adv. Rheumatol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1148\/radiol.2021203783","article-title":"Development and validation of a radiomics model for differentiating bone islands and osteoblastic bone metastases at abdominal CT","volume":"299","author":"Hong","year":"2021","journal-title":"Radiology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1148\/radiol.2018172300","article-title":"Mr imaging of rectal cancer: Radiomics analysis to assess treatment response after neoadjuvant therapy","volume":"287","author":"Horvat","year":"2018","journal-title":"Radiology"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Klontzas, M.E., Manikis, G.C., Nikiforaki, K., Vassalou, E.E., Spanakis, K., Stathis, I., Kakkos, G.A., Matthaiou, N., Zibis, A.H., and Marias, K. (2021). Radiomics and machine learning can differentiate transient osteoporosis from avascular necrosis of the hip. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091686"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e02085","DOI":"10.1002\/brb3.2085","article-title":"Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning","volume":"11","author":"Xu","year":"2021","journal-title":"Brain Behav."},{"key":"ref_27","unstructured":"(2023, May 27). Numpy. Available online: https:\/\/numpy.org."},{"key":"ref_28","unstructured":"(2023, May 27). Matplotlib. Available online: https:\/\/matplotlib.org."},{"key":"ref_29","unstructured":"(2023, May 25). XGBoost. Available online: https:\/\/xgboost.readthedocs.io\/en\/stable\/tutorials\/model.html."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1136\/ard.2009.110767","article-title":"Defining active sacroiliitis on magnetic resonance imaging (MRI) for classification of axial spondyloarthritis: A consensual approach by the ASAS\/OMERACT MRI group","volume":"68","author":"Rudwaleit","year":"2009","journal-title":"Ann. Rheum Dis."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1136\/annrheumdis-2014-206971","article-title":"EULAR recommendations for the use of imaging in the diagnosis and management of spondyloarthritis in clinical practice","volume":"74","author":"Mandl","year":"2015","journal-title":"Ann. Rheum. Dis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.mri.2021.06.003","article-title":"Superficial fibromatosis: MRI radiomics and T2 mapping correlate with treatment response","volume":"81","author":"Ramachandran","year":"2021","journal-title":"Magn. Reson. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.ejrad.2009.04.034","article-title":"Magnetic resonance imaging of active sacroiliitis: Do we really need gadolinium?","volume":"71","author":"Althoff","year":"2009","journal-title":"Eur. J. Radiol."},{"key":"ref_34","first-page":"225","article-title":"Gadolinium contrast-enhanced MRI sequence does not have an incremental value in the assessment of sacroiliitis in patients with early inflammatory back pain by using MRI in combination with pelvic radiographs: A 2-year follow-up study","volume":"32","author":"Jurik","year":"2014","journal-title":"Clin. Exp. Rheumatol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20170090","DOI":"10.1259\/bjr.20170090","article-title":"MRI assessment of sacroiliitis for the diagnosis of axial spondyloarthropathy: Comparison of fat-saturated T2, STIR and contrast-enhanced sequences","volume":"90","author":"Sung","year":"2017","journal-title":"Br. J. Radiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1007\/s11548-020-02219-7","article-title":"A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis","volume":"15","author":"Faleiros","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1093\/rheumatology\/keab542","article-title":"A predictive clinical-radiomics nomogram for diagnosing of axial spondyloarthritis using MRI and clinical risk factors","volume":"61","author":"Ye","year":"2022","journal-title":"Rheumatology"},{"key":"ref_38","unstructured":"D\u2019Antonoli, T.A., and Mercaldo, N.D. Obsolescence of nomograms in radiomics research. Eur. Radiol., 2023."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1007\/s10067-023-06543-6","article-title":"Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis?","volume":"42","author":"Zheng","year":"2023","journal-title":"Clin. Rheumatol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1136\/ard.2007.084244","article-title":"Development and validation of the Spondyloarthritis Research Consortium of Canada (SPARCC) Enthesitis Index","volume":"68","author":"Maksymowych","year":"2009","journal-title":"Ann. Rheum. Dis."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/15\/2587\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:25:07Z","timestamp":1760127907000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/15\/2587"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,3]]},"references-count":40,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["diagnostics13152587"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13152587","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,3]]}}}