ArticleJournal of orthopaedic research : official publication of the Orthopaedic Research Society2025
MRI-based radiomic analysis of soft tissue reactions near total hip arthroplasty.
Article in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Radiograph-Based Radiomics Successfully Classify MRI-Based Identification of Femoral Loosening in Patients With Total Hip Arthroplasty-A Preliminary Study.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Article
- Multi-Spectral MRI-Based Radiomic Features Can Classify Femoral Loosening in Patients With Total Hip Arthroplasty.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Article
- Exploratory analysis of infrapatellar fat pad MRI-based radiomics for detection of knee structure abnormalities in collegiate basketball players and swimmers.Osteoarthritis imaging · 2025Article
- Comparison of Artificial Intelligence Models Using CT Radiomics for Predicting Post-Vertebral Augmentation Residual Back Pain in Osteoporotic Vertebral Compression Fractures.International journal of medical sciences · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This study applied radiomics to MRI data for automated classification of soft tissue abnormalities near total hip arthroplasty (THA). A total of 126 subjects with 1.5 T MRI of symptomatic THA were included in the analysis. Peri-prosthetic soft tissue regions of interest were manually segmented and classified by an expert radiologist. An established radiomics library was used to extract 96 features from 2D image patches across segmented regions. Logistic regression was employed as the primary radiomic classifier, achieving an average area under curve (AUC) of 0.71 in differentiating tissue classifications spanning normal, infected, and several inflammatory, noninfectious categories. Notably, infection cases were identified with the highest accuracy, attaining an AUC of 0.79. Statement of Clinical Significance: This study demonstrates that radiomics applied to MRI data can effectively automate the classification of soft tissue abnormalities in symptomatic total hip arthroplasty, particularly in differentiating periprosthetic infections.
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Registered trials
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