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基于 MRI 的子宫肌瘤 FIGO 分类系统影像学综述。

MRI-based pictorial review of the FIGO classification system for uterine fibroids.

机构信息

The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe St., MRI Building 143, Baltimore, MD, 21287, USA.

Department of Radiology, Kaiser Permanente Mid-Atlantic Permanente Medical Group, Rockville, MD, USA.

出版信息

Abdom Radiol (NY). 2021 May;46(5):2146-2155. doi: 10.1007/s00261-020-02882-z. Epub 2021 Jan 1.

Abstract

Uterine fibroids are the most common gynecologic neoplasm and contribute to significant morbidity, particularly when submucosal in location or large enough to cause bulk symptoms. Correctly classifying fibroids is essential for treatment planning and prevention of complications. Ultrasound is the first-line imaging modality for characterizing uterine fibroids. However, MRI allows for high-resolution, multiplanar visualization of leiomyomata that affords a more accurate assessment than ultrasound, particularly when fibroids are numerous. The FIGO system was developed in order to more uniformly and consistently describe and classify uterine fibroids. In this article, we review the MRI appearance of each of the FIGO classification types, detailing key features to report. Additionally, we present a proposed template for structured reporting of uterine fibroids based on the FIGO classification system.

摘要

子宫肌瘤是最常见的妇科肿瘤,会导致严重的发病率,特别是当位于黏膜下或足够大引起肿块症状时。正确分类子宫肌瘤对于治疗计划和预防并发症至关重要。超声是子宫肌瘤特征描述的一线影像学方法。然而,磁共振成像(MRI)允许对子宫肌瘤进行高分辨率、多平面可视化,比超声能提供更准确的评估,尤其是当肌瘤数量较多时。FIGO 系统是为了更统一和一致地描述和分类子宫肌瘤而开发的。在本文中,我们回顾了 FIGO 分类系统中每一种类型的 MRI 表现,详细介绍了报告的关键特征。此外,我们还根据 FIGO 分类系统提出了一个子宫肌瘤结构化报告的模板。

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