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妇产科影像报告和数据系统(GI-RADS):诊断性能和多轮次审阅者间的一致性。

Gynecology Imaging Reporting and Data System (GI-RADS): diagnostic performance and inter-reviewer agreement.

机构信息

Department of Radiodiagnosis, Zagazig University, Zagazig, Egypt.

Department of Radiodiagnosis, Ain Shams University, Cairo, Egypt.

出版信息

Eur Radiol. 2019 Nov;29(11):5981-5990. doi: 10.1007/s00330-019-06181-0. Epub 2019 Apr 16.

Abstract

OBJECTIVE

To evaluate diagnostic performance and inter-reviewer agreement (IRA) of the Gynecologic Imaging Reporting and Data System (GI-RADS) for diagnosis of adnexal masses (AMs) by pelvic ultrasound (US).

PATIENTS AND METHODS

A prospective multicenter study included 308 women (mean age, 41 ± 12.5 years; range, 15-73 years) with 325 AMs detected by US. All US examinations were analyzed, and AMs were categorized into five categories according to the GI-RADS classification. We used histopathology and US follow-up as the reference standards for calculating diagnostic performance of GI-RADS for detecting malignant AMs. The Fleiss kappa (κ) tests were applied to evaluate the IRA of GI-RADS scoring results for predicting malignant AMs.

RESULTS

A total of 325 AMs were evaluated: 127 (39.1%) were malignant and 198 (60.9%) were benign. Of 95 AMs categorized as GI-RADS 2 (GR2), none was malignant; of 94 AMs categorized as GR3, three were malignant; of 13 AMs categorized as GR4, six were malignant; and of 123 AMs categorized as GR5, 118 were malignant. On a lesion-based analysis, the GI-RADS had a sensitivity, a specificity, and an accuracy of 92.9%, 97.5%, and 95.7%, respectively, when regarding only those AMs classified as GR5 for predicting malignancy. Considering combined GR4 and GR5 as a predictor for malignancy, the sensitivity, specificity, and accuracy of GI-RADS were 97.6%, 93.9%, and 95.4%, respectively. The IRA of the GI-RADS category was very good (κ = 0.896). The best cutoff value for predicting malignant AMs was >GR3.

CONCLUSIONS

The GI-RADS is very valuable for improving US structural reports.

KEY POINTS

• There is still a lack of a standard in the assessment of AMs. • GI-RADS is very valuable for improving US structural reports of AMs. • GI-RADS criteria are easy and work at least as well as IOTA.

摘要

目的

评估妇科影像学报告和数据系统(GI-RADS)在经盆腔超声(US)诊断附件肿块(AMs)中的诊断性能和观察者间一致性(IRA)。

患者与方法

这是一项前瞻性多中心研究,纳入了 308 名女性(平均年龄 41±12.5 岁;范围 15-73 岁),共检出 325 个 AMs。所有 US 检查均进行了分析,并根据 GI-RADS 分类将 AMs 分为五类。我们将组织病理学和 US 随访作为计算 GI-RADS 检测恶性 AMs 的诊断性能的参考标准。采用 Fleiss kappa(κ)检验评估 GI-RADS 评分结果预测恶性 AMs 的 IRA。

结果

共评估了 325 个 AMs:127 个(39.1%)为恶性,198 个(60.9%)为良性。95 个被归类为 GI-RADS 2(GR2)的 AMs 中无一例为恶性;94 个被归类为 GR3 的 AMs 中有 3 个为恶性;13 个被归类为 GR4 的 AMs 中有 6 个为恶性;123 个被归类为 GR5 的 AMs 中有 118 个为恶性。在基于病变的分析中,仅将那些被归类为 GR5 的 AMs 视为恶性预测因素时,GI-RADS 的敏感性、特异性和准确性分别为 92.9%、97.5%和 95.7%。当将 GR4 和 GR5 联合作为恶性预测因素时,GI-RADS 的敏感性、特异性和准确性分别为 97.6%、93.9%和 95.4%。GI-RADS 分类的 IRA 非常好(κ=0.896)。预测恶性 AMs 的最佳截断值为>GR3。

结论

GI-RADS 非常有助于提高 US 结构报告的质量。

关键点

• AMs 的评估仍然缺乏标准。• GI-RADS 非常有助于提高 AMs 的 US 结构报告质量。• GI-RADS 标准简单,至少与 IOTA 一样有效。

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