SynthesisJapanese journal of radiology2026
MRI for diagnosing uterine sarcoma: a systematic review and meta-analysis.
Synthesis in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
2 citing papers in PubMed.
- Multicenter Study of MRI Features in Uterine Myxoid Leiomyosarcoma: Revealing and Overcoming a Critical Pitfall in Current Consensus Criteria.Korean journal of radiology · 2026Article
- Deep learning reconstruction-applied PROPELLER diffusion-weighted imaging in uterine malignancies: a comparison study with MUSE.Japanese journal of radiology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo systematically review and meta-analyze the diagnostic accuracy of magnetic resonance imaging in differentiating uterine sarcomas from benign leiomyomas, thereby providing updated evidence for the revision of the Japan Radiological Society Diagnostic Imaging Guidelines. EVIDENCE ACQUISITION: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies guidelines, a comprehensive literature search was performed by an independent information specialist to identify studies published between July 2019 and April 2025. Inclusion criteria required studies evaluating MRI (the combination of T2-weighted imaging and at least one additional sequence (diffusion-weighted imaging and/or contrast-enhanced imaging)) in patients with myometrial masses suspicious for sarcoma, with diagnostic performance assessable by 2 × 2 contingency tables. The reference standard was histopathology for malignant lesions, whereas benign diagnoses could be established by histopathology or by clinical/imaging follow-up. Data extraction and Quality Assessment of Diagnostic Accuracy Studies-2 quality assessment were performed independently. Pooled sensitivity and specificity were estimated using a random-effects model, and the hierarchical summary receiver operating characteristic (HSROC) curve was generated to estimate the area under the curve (AUC). EVIDENCE SYNTHESIS: Twelve studies met the inclusion criteria. All were retrospective, and substantial heterogeneity existed in patient selection and MRI protocols. The pooled sensitivity of MRI for diagnosing sarcoma was 84.2% (95% confidence interval (CI), 76.3–89.9%), and the pooled specificity was 89.1% (95% CI, 78.1–94.9%). The HSROC-derived AUC was 0.916 (95% CI, 0.876–0.941), indicating high overall diagnostic accuracy. Considerable statistical heterogeneity was noted (I2 = 66.3% for sensitivity and I2 = 95.8% for specificity).
conclusionMRI demonstrates high diagnostic accuracy in differentiating sarcoma from leiomyoma. However, substantial heterogeneity in study design, imaging protocols, and diagnostic criteria limits overall consistency across studies. Standardized MRI protocols and diagnostic criteria are essential to improve diagnostic uniformity and clinical applicability.
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