| Literature DB >> 34997373 |
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.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