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Meta-Analysis
. 2024 Apr;165(1):22-33.
doi: 10.1002/ijgo.15136. Epub 2023 Sep 21.

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta-analysis

Affiliations
Meta-Analysis

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta-analysis

Antonio Raffone et al. Int J Gynaecol Obstet. 2024 Apr.

Abstract

Background: Differential diagnosis between uterine leiomyomas and sarcomas is challenging. Magnetic resonance imaging (MRI) represents the second-line diagnostic method after ultrasound for the assessment of uterine masses.

Objectives: To assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas.

Search strategy: A systematic review and meta-analysis was performed searching five electronic databases from their inception to June 2023.

Selection criteria: All peer-reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma, which also comprehended a preoperative MRI evaluation of the uterine mass.

Data collection and analysis: Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic of MRI in differentiating uterine leiomyomas and sarcomas were calculated as individual and pooled estimates, with 95% confidence intervals (CI).

Results: Eight studies with 2495 women (2253 with uterine leiomyomas and 179 with uterine sarcomas), were included. MRI showed pooled sensitivity of 0.90 (95% CI 0.84-0.94), specificity of 0.96 (95% CI 0.96-0.97), positive likelihood ratio of 13.55 (95% CI 6.20-29.61), negative likelihood ratio of 0.08 (95% CI 0.02-0.32), diagnostic odds ratio of 175.13 (95% CI 46.53-659.09), and area under the curve of 0.9759.

Conclusions: MRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas.

Keywords: Leiomyosarcoma; Myomata; malignancy; neoplasia; prediction; preoperative assessment; uterus.

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