ArticleLa Radiologia medica2023
T1-weighted images-based radiomics for structural lesions evaluation in patients with suspected axial spondyloarthritis.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 8 citations in OpenAlex.
- CT-guided sacroiliac joint injection for chronic sacroiliitis: outcomes and predictors in a retrospective cohort study.La Radiologia medica · 2026Article
- A Comprehensive Clinical Decision Support System for the Early Diagnosis of Axial Spondyloarthritis: Multi-Sequence MRI, Clinical Risk Integration, and Explainable Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Biomarkers in axial spondyloarthritis diagnosis: from clinical signs to multi-omics integration.Frontiers in immunology · 2026Review
- The evaluation of effectiveness of IL-17 and IL-23 inhibitors on nail and enthesis involvement in early psoriatic arthritis patients by high-frequency ultrasonography: a single-centre prospective proof-of-concept study.La Radiologia medica · 2025Article
- Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?La Radiologia medica · 2025Review
- Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.Journal of clinical medicine · 2024Review
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9 authors at 2 institutions in 1 country.
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Abstract
purposeThe aim of this study was to investigate the feasibility of radiomics based on T1-weighted images (T1WI) for assessing sacroiliac joint (SIJ) structural lesions in patients with suspected axial spondyloarthritis (axSpA). MATERIALS AND
methodsA total of 266 patients with clinical suspicion of axSpA between December 2016 and January 2022 were enrolled. Structural lesions were assessed on low-dose CT (ldCT) and MRI, respectively. Radiomic features, extracted from SIJ T1WI, were included to generate the radiomics model. The performance of the radiomics model was evaluated using receiver operating characteristic (ROC) curve. Furthermore, point-biserial correlation analysis was used to interpret the associations between the radiomic feature and structural lesions.
resultsUsing ldCT as the reference standard, the radiomics model showed favorable performance for detecting positive global structural lesions in the training cohort (AUC, 0.82 [95% CI: 0.76, 0.88]) and validation cohort (AUC, 0.82 [95% CI: 0.72, 0.91]. Experienced MRI raters yielded predictive AUCs of 0.73 (95% CI: 0.67, 0.79), and 0.74 (95% CI: 0.66, 0.83) in the training and validation cohort, respectively. The seven radiomic features included in the radiomics model showed significant correlation with different kinds of structural lesions (P all < 0.05). Among them, Wavelet.LHL_firstorder_90Percentile showed the strongest association with fat lesion (r = 0.48, P < 0.05).
conclusionThe radiomics analysis with T1WI could effectively detect SIJ structural lesions and achieved expert-level performance. Each radiomic feature was correlated with different structural lesions significantly, which might inform radiomic-based applications for axSpA intelligent diagnosis.
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