Literature DB >> 34997373

Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.

Ariane Priscilla Magalhães Tenório1, José Raniery Ferreira-Junior2, Vitor Faeda Dalto2, Matheus Calil Faleiros2, Rodrigo Luppino Assad2, Paulo Louzada-Junior2, Marcello Henrique Nogueira-Barbosa2,3, Rangaraj Mandayam Rangayyan4, Paulo Mazzoncini de Azevedo-Marques2.   

Abstract

Spondyloarthritis (SpA) is a group of diseases primarily involving chronic inflammation of the spine and peripheral joints, as evaluated by magnetic resonance imaging (MRI). Considering the complexity of SpA, we performed a retrospective study to discover quantitative/radiomic MRI-based features correlated with SpA. We also investigated different fat-suppression MRI techniques to develop detection models for inflammatory sacroiliitis. Finally, these model results were compared with those of experienced musculoskeletal radiologists, and the concordance level was evaluated. Examinations of 46 consecutive patients were obtained using SPAIR (spectral attenuated inversion recovery) and STIR (short tau inversion recovery) MRI sequences. Musculoskeletal radiologists manually segmented the sacroiliac joints for further extraction of 230 MRI features from gray-level histogram/matrices and wavelet filters. These features were associated with sacroiliitis, SpA, and the current biomarkers of ESR (erythrocyte sedimentation rate), CRP (C-reactive protein), BASDAI (Bath Ankylosing Spondylitis Activity Index), BASFI (Bath Ankylosing Spondylitis Functional Index), and MASES (Maastricht Ankylosing Spondylitis Enthesis Score). The Mann-Whitney U test showed that the radiomic markers from both MRI sequences were associated with active sacroiliitis and with SpA and its axial and peripheral subtypes (p < 0.05). Spearman's coefficient also identified a correlation between MRI markers and data from clinical practice (p < 0.05). Fat-suppression MRI models yielded performances that were statistically equivalent to those of specialists and presented strong concordance in identifying inflammatory sacroiliitis. SPAIR and STIR acquisition protocols showed potential for the evaluation of sacroiliac joints and the composition of a radiomic model to support the clinical assessment of SpA.
© 2021. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.

Entities:  

Keywords:  Artificial intelligence; Radiomics; Sacroiliitis; Spondyloarthritis

Mesh:

Substances:

Year:  2022        PMID: 34997373      PMCID: PMC8854535          DOI: 10.1007/s10278-021-00559-7

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  26 in total

Review 1.  Spondyloarthritis: from unifying concepts to improved treatment.

Authors:  Jacqueline E Paramarta; Dominique Baeten
Journal:  Rheumatology (Oxford)       Date:  2013-12-24       Impact factor: 7.580

Review 2.  Role of Imaging in the Era of Precision Medicine.

Authors:  Angela Giardino; Supriya Gupta; Emmi Olson; Karla Sepulveda; Leon Lenchik; Jana Ivanidze; Rebecca Rakow-Penner; Midhir J Patel; Rathan M Subramaniam; Dhakshinamoorthy Ganeshan
Journal:  Acad Radiol       Date:  2017-01-25       Impact factor: 3.173

3.  Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging.

Authors:  Matheus Calil Faleiros; Marcello Henrique Nogueira-Barbosa; Vitor Faeda Dalto; José Raniery Ferreira Júnior; Ariane Priscilla Magalhães Tenório; Rodrigo Luppino-Assad; Paulo Louzada-Junior; Rangaraj Mandayam Rangayyan; Paulo Mazzoncini de Azevedo-Marques
Journal:  Adv Rheumatol       Date:  2020-05-07

4.  Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Authors:  Atul Deodhar; Martin Rozycki; Cody Garges; Oodaye Shukla; Theresa Arndt; Tara Grabowsky; Yujin Park
Journal:  Clin Rheumatol       Date:  2019-05-01       Impact factor: 2.980

5.  The Assessment of SpondyloArthritis International Society classification criteria for peripheral spondyloarthritis and for spondyloarthritis in general.

Authors:  M Rudwaleit; D van der Heijde; R Landewé; N Akkoc; J Brandt; C T Chou; M Dougados; F Huang; J Gu; Y Kirazli; F Van den Bosch; I Olivieri; E Roussou; S Scarpato; I J Sørensen; R Valle-Oñate; U Weber; J Wei; J Sieper
Journal:  Ann Rheum Dis       Date:  2010-11-24       Impact factor: 19.103

6.  Comparison between STIR and T2-weighted SPAIR sequences in the evaluation of inflammatory sacroiliitis: diagnostic performance and signal-to-noise ratio.

Authors:  Vitor Faeda Dalto; Rodrigo Luppino Assad; Mario Müller Lorenzato; Michel Daoud Crema; Paulo Louzada-Junior; Marcello Henrique Nogueira-Barbosa
Journal:  Radiol Bras       Date:  2020 Jul-Aug

Review 7.  The concept of spondyloarthritis: where are we now?

Authors:  Neha Garg; Filip van den Bosch; Atul Deodhar
Journal:  Best Pract Res Clin Rheumatol       Date:  2014-11-18       Impact factor: 4.098

8.  The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis.

Authors:  J Sieper; M Rudwaleit; X Baraliakos; J Brandt; J Braun; R Burgos-Vargas; M Dougados; K-G Hermann; R Landewé; W Maksymowych; D van der Heijde
Journal:  Ann Rheum Dis       Date:  2009-06       Impact factor: 19.103

9.  Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer.

Authors:  Stephen S F Yip; Ying Liu; Chintan Parmar; Qian Li; Shichang Liu; Fangyuan Qu; Zhaoxiang Ye; Robert J Gillies; Hugo J W L Aerts
Journal:  Sci Rep       Date:  2017-06-14       Impact factor: 4.379

Review 10.  The Biological Meaning of Radiomic Features.

Authors:  Michal R Tomaszewski; Robert J Gillies
Journal:  Radiology       Date:  2021-01-05       Impact factor: 11.105

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