{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T01:19:52Z","timestamp":1788830392451,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"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>Objectives: We wished to compare the diagnostic performance of texture analysis (TA) against that of a visual qualitative assessment in identifying early sacroiliitis (nr-axSpA). Methods: A total of 92 participants were retrospectively included at our university hospital institution, comprising 30 controls and 62 patients with axSpA, including 32 with nr-axSpA and 30 with r-axSpA, who underwent MR examination of the sacroiliac joints. MRI at 3T of the lumbar spine and the sacroiliac joint was performed using oblique T1-weighted (W), fluid-sensitive, fat-saturated (Fs) T2WI images. The modified New York criteria for AS were used. Patients were classified into the nr-axSpA group if their digital radiography (DR) and\/or CT results within 7 days from the MR examination showed a DR and\/or CT grade &lt; 2 for the bilateral sacroiliac joints or a DR and\/or CT grade &lt; 3 for the unilateral sacroiliac joint. Patients were classified into the r-axSpA group if their DR and\/or CT grade was 2 to 3 for the bilateral sacroiliac joints or their DR and\/or CT grade was 3 for the unilateral sacroiliac joint. Patients were considered to have a confirmed diagnosis if their DR or CT grade was 4 for the sacroiliac joints and were thereby excluded. A control group of healthy individuals matched in terms of age and sex to the patients was included in this study. First, two readers independently qualitatively scored the oblique coronal T1WI and FsT2WI non-enhanced sacroiliac joint images. The diagnostic efficacies of the two readers were judged and compared using an assigned Likert score, conducting a Kappa consistency test of the diagnostic results between two readers. Texture analysis models (the T1WI-TA model and the FsT2WI-TA model) were constructed through feature extraction and feature screening. The qualitative and quantitative results were evaluated for their diagnostic performance and compared against a clinical reference standard. Results: The qualitative scores of the two readers could significantly distinguish between the healthy controls and the nr-axSpA group and the nr-axSpA and r-axSpA groups (both p &lt; 0.05). Both TA models could significantly distinguish between the healthy controls and the nr-axSpA group and the nr-axSpA group and the r-axSpA group (both p &lt; 0.05). There was no significant difference in the differential diagnoses of the two TA models between the healthy controls and the nr-axSpA group (AUC: 0.934 vs. 0.976; p = 0.1838) and between the nr-axSpA and r-axSpA groups (AUC: 0.917 vs. 0.848; p = 0.2592). In terms of distinguishing between the healthy control and nr-axSpA groups, both the TA models were superior to the qualitative scores of the two readers (all p &lt; 0.05). In terms of distinguishing between the nr-axSpA and r-axSpA groups, the T1WI-TA model was superior to the qualitative scores of the two readers (p = 0.023 and p = 0.007), whereas there was no significant difference between the fsT2WI-TA model and the qualitative scores of the two readers (p = 0.134 and p = 0.065). Conclusions: Based on MR imaging, the T1WI-TA and fsT2WI-TA models were highly effective for the early diagnosis of sacroiliac joint arthritis. The T1WI-TA model significantly improved the early diagnostic efficacy for sacroiliac arthritis compared to that of the qualitative scores of the readers, while the efficacy of the fsT2WI-TA model was comparable to that of the readers.<\/jats:p>","DOI":"10.3390\/diagnostics15020209","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T10:27:58Z","timestamp":1737109678000},"page":"209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Differentiation of Early Sacroiliitis Using Machine-Learning- Supported Texture Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0666-8215","authenticated-orcid":false,"given":"Qingqing","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3731-9801","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Dang","sequence":"additional","affiliation":[{"name":"Department of Rheumatology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojing","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liye","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9859-5860","authenticated-orcid":false,"given":"Hongjie","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,17]]},"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":"1550","DOI":"10.1136\/annrheumdis-2019-215589","article-title":"MRI lesions in the sacroiliac joints of patients with spondyloarthritis: An update of definitions and validation by the ASAS MRI working group","volume":"78","author":"Maksymowych","year":"2019","journal-title":"Ann. Rheum. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1136\/ard-2024-225882","article-title":"Development of international consensus on a standardised image acquisition protocol for diagnostic evaluation of the sacroiliac joints by MRI: An ASAS\u2013SPARTAN collaboration","volume":"83","author":"Lambert","year":"2024","journal-title":"Ann. Rheum. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1097\/RLI.0000000000000486","article-title":"Magnetic resonance imaging\u2013based grading of cartilaginous bone tumors: Added value of quantitative texture analysis","volume":"53","author":"Fritz","year":"2018","journal-title":"Investig. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8364","DOI":"10.1007\/s00330-022-08897-y","article-title":"Adjacent cartilage tissue structure after successful transplantation: A quantitative MRI study using T2 mapping and texture analysis","volume":"32","author":"Janacova","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1148\/radiol.212009","article-title":"MRI-based texture analysis of infrapatellar fat pad to predict knee osteoarthritis incidence","volume":"304","author":"Li","year":"2022","journal-title":"Radiology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1002\/acr.20312","article-title":"Assessment of structural lesions in sacroiliac joints enhances diagnostic utility of magnetic resonance imaging in early spondylarthritis","volume":"62","author":"Weber","year":"2010","journal-title":"Arthritis Care Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3048","DOI":"10.1002\/art.27571","article-title":"The diagnostic utility of magnetic resonance imaging in spondylarthritis: An international multicenter evaluation of one hundred eighty-seven subjects","volume":"62","author":"Weber","year":"2010","journal-title":"Arthritis Rheum."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e004044","DOI":"10.1136\/rmdopen-2023-004044","article-title":"Diagnostic accuracy in axial spondyloarthritis: A systematic evaluation of the role of clinical information in the interpretation of sacroiliac joint imaging","volume":"10","author":"Pohlner","year":"2024","journal-title":"RMD Open"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1546-0096-12-25","article-title":"Magnetic resonance imaging findings in juvenile spondyloarthropathy and effects of treatment observed on subsequent imaging","volume":"12","author":"Lin","year":"2014","journal-title":"Pediatr. Rheumatol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1136\/annrheumdis-2016-210640","article-title":"Comparison of MRI with radiography for detecting structural lesions of the sacroiliac joint using CT as standard of reference: Results from the SIMACT study","volume":"76","author":"Diekhoff","year":"2017","journal-title":"Ann. Rheum. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1002\/art.38283","article-title":"Spinal inflammation in the absence of sacroiliac joint inflammation on magnetic resonance imaging in patients with active nonradiographic axial spondyloarthritis","volume":"66","author":"Sieper","year":"2014","journal-title":"Arthritis Rheumatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s00330-021-08171-7","article-title":"MRI and spondyloarthropathy: Diagnostic performance compared to long-term clinical follow-up with evaluation of gadolinium chelates injection","volume":"32","author":"Meunier","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"520","DOI":"10.2214\/AJR.18.20624","article-title":"Texture analysis of imaging: What radiologists need to know","volume":"212","author":"Varghese","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"ref_18","first-page":"2023","article-title":"Enhanced transfer learning strategies for effective kidney tumor classification with CT imaging","volume":"14","author":"Majid","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khan, F., Gulzar, Y., Ayoub, S., Majid, M., Mir, M.S., and Soomro, A.B. (2023). Least square-support vector machine based brain tumor classification system with multi model texture features. Front. Appl. Math. Stat., 9.","DOI":"10.3389\/fams.2023.1324054"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/RBME.2021.3115703","article-title":"Texture analysis and its applications in biomedical imaging: A survey","volume":"15","author":"Ghalati","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3174\/ajnr.A2061","article-title":"Texture analysis: A review of neurologic MR imaging applications","volume":"31","author":"Kassner","year":"2010","journal-title":"Am. J. Neuroradiol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Khan, F., Ayoub, S., Gulzar, Y., Majid, M., Reegu, F.A., Mir, M.S., Soomro, A.B., and Elwasila, O. (2023). MRI-based effective ensemble frameworks for predicting human brain tumor. J. Imaging, 9.","DOI":"10.3390\/jimaging9080163"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1007\/s00261-021-03359-3","article-title":"A primer on texture analysis in abdominal radiology","volume":"47","author":"Horvat","year":"2022","journal-title":"Abdom. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/jmri.28760","article-title":"Imaging assessment of the efficacy of chemotherapy in primary malignant bone tumors: Recent advances in qualitative and quantitative magnetic resonance imaging and radiomics","volume":"59","author":"Liu","year":"2024","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ejrad.2018.02.024","article-title":"Texture analysis of apparent diffusion coefficient maps for treatment response assessment in prostate cancer bone metastases\u2014A pilot study","volume":"101","author":"Reischauer","year":"2018","journal-title":"Eur. J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109460","DOI":"10.1016\/j.ejrad.2020.109460","article-title":"Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy","volume":"134","author":"Felisaz","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5669","DOI":"10.1007\/s00330-021-07705-3","article-title":"Evaluation of microvascular permeability of skeletal muscle and texture analysis based on DCE-MRI in alloxan-induced diabetic rabbits","volume":"31","author":"Liu","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1007\/s00330-018-5846-8","article-title":"Vertebral body insufficiency fractures: Detection of vertebrae at risk on standard CT images using texture analysis and machine learning","volume":"29","author":"Muehlematter","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1148\/radiol.14131390","article-title":"Assessment of risk of femoral neck fracture with radiographic texture parameters: A retrospective study","volume":"272","author":"Thevenot","year":"2014","journal-title":"Radiology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s13075-022-02743-8","article-title":"Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: Data from OAI and MOST cohorts","volume":"24","author":"Nguyen","year":"2022","journal-title":"Arthritis Res. Ther."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e121","DOI":"10.1136\/annrheumdis-2021-220008","article-title":"Correspondence on \u2018MRI lesions in the sacroiliac joints of patients with spondyloarthritis: An update of definitions and validation by the ASAS MRI working group\u2019","volume":"82","author":"Jibri","year":"2023","journal-title":"Ann. Rheum. Dis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s11282-022-00641-y","article-title":"Magnetic resonance imaging texture analysis for quantitative evaluation of the mandibular condyle in juvenile idiopathic arthritis","volume":"39","author":"Ricardo","year":"2023","journal-title":"Oral Radiol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/2\/209\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:30:59Z","timestamp":1759919459000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/2\/209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["diagnostics15020209"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15020209","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,17]]}}}