Evidence map›Paper›PMID 41678008›Full record

SynthesisJapanese journal of radiology2026

MRI for diagnosing uterine sarcoma: a systematic review and meta-analysis.

Tsukasa Saida, Takahiro Ueda, Maho Kurihara, Mariko Kumazawa, Fuki Shitano, Shinya Fujii

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tsukasa SaidaDepartment of Radiology, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan. saida_sasaki_tsukasa@md.tsukuba.ac.jp.ORCID http://orcid.org/0000-0003-4530-7375
Takahiro UedaDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.ORCID http://orcid.org/0000-0003-2577-3619
Maho KuriharaDepartment of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.ORCID http://orcid.org/0009-0003-8778-2710
Mariko KumazawaDepartment of Radiology, Dokkyo Medical University School of Medicine, 880 Kitakobayashi, Mibu-machi, Tochigi, Shimotsuga-gun, 321-0293, Japan.ORCID http://orcid.org/0000-0002-9470-1099
Fuki ShitanoDepartment of Radiology, Osaka Red Cross Hospital, 5-30 Fudegasakicho, Tennoji-ku, Osaka, 543-8555, Japan.ORCID http://orcid.org/0000-0002-9137-431X
Shinya FujiiDivision of Radiology, Department of Pathophysiological and Therapeutic Science, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago, Tottori, 683-8504, Japan.ORCID http://orcid.org/0000-0002-0048-0396

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Magnetic Resonance ImagingSarcomaUterine NeoplasmsDiagnosis, DifferentialFemaleHumansLeiomyomaSensitivity and SpecificityApparent diffusion coefficientLeiomyosarcomasMagnetic resonance imagingMyomaUterus

Identifiers

PMID41678008
PMCPMC13222180

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.