Suppr超能文献

对比增强超声 LI-RADS 2017:与 CT/MRI LI-RADS 的比较。

Contrast-enhanced ultrasound LI-RADS 2017: comparison with CT/MRI LI-RADS.

机构信息

Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.

Department of Ultrasound, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Artificial Cell, Artificial Cell Engineering Technology Research Center of Public Health Ministry, Tianjin Third Central Hospital, Tianjin, 300170, China.

出版信息

Eur Radiol. 2021 Feb;31(2):847-854. doi: 10.1007/s00330-020-07159-z. Epub 2020 Aug 15.

Abstract

OBJECTIVE

To compare the classification based on contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) with that of contrast-enhanced CT and MRI (CECT/MRI) LI-RADS for liver nodules in patients at high risk of hepatocellular carcinoma.

METHODS

Two hundred thirty-nine patients with 273 nodules were enrolled in this retrospective study. Each nodule was categorized according to the CEUS LI-RADS version 2017 and CECT/MRI LI-RADS version 2017. The diagnostic performance of CEUS and CECT/MRI was compared. The reference standard was histopathology diagnosis. Inter-modality agreement was assessed with Cohen's kappa.

RESULTS

The inter-modality agreement for CEUS LI-RADS and CECT/MRI LI-RADS was fair with a kappa value of 0.319 (p < 0.001). The positive predictive values (PPVs) of hepatocellular carcinoma (HCC) in LR-5, LR-4, and LR-3 were 98.3%, 60.0%, and 25.0% in CEUS, and 95.9%, 65.7%, and 48.1% in CECT/MRI, respectively. The sensitivities and specificities of LR-5 for diagnosing HCC were 75.6% and 93.8% in CEUS, and 83.6% and 83.3% in CECT/MRI, respectively. The positive predictive values of non-HCC malignancy in CEUS LR-M and CECT/MRI LR-M were 33.9% and 93.3%, respectively. The sensitivity, specificity, and accuracy for diagnosing non-HCC malignancy were 90.9%, 84.5%, and 85.0% in CEUS LR-M and 63.6%, 99.6%, and 96.7% in CECT/MRI LR-M, respectively.

CONCLUSIONS

The inter-modality agreement of the LI-RADS category between CEUS and CECT/MRI is fair. The positive predictive values of HCCs in LR-5 of the CEUS and CECT/MRI LI-RADS are comparable. CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M.

KEY POINTS

• The inter-modality agreement for the final LI-RADS category between CEUS and CECT/MRI is fair. • The LR-5 of CEUS and CECT/MRI LI-RADS corresponds to comparable positive predictive values (PPVs) of HCC. For LR-3 and LR-4 nodules categorized by CECT/MRI, CEUS examination should be performed, at least if they can be detected on plain ultrasound. • CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M. For LR-M nodules categorized by CEUS, re-evaluation by CECT/MRI is necessary.

摘要

目的

比较基于超声造影(CEUS)肝脏成像报告和数据系统(LI-RADS)与基于对比增强 CT 和 MRI(CECT/MRI)LI-RADS 的分类方法在肝细胞癌高危患者肝脏结节中的应用。

方法

本回顾性研究纳入了 239 例 273 个结节的患者。每个结节均根据 CEUS LI-RADS 版本 2017 和 CECT/MRI LI-RADS 版本 2017 进行分类。比较了 CEUS 和 CECT/MRI 的诊断性能。以组织病理学诊断为参考标准。采用 Cohen's kappa 评估两种方法间的一致性。

结果

CEUS LI-RADS 和 CECT/MRI LI-RADS 间的一致性为中等,kappa 值为 0.319(p<0.001)。在 CEUS 中,LR-5、LR-4 和 LR-3 中肝细胞癌(HCC)的阳性预测值(PPV)分别为 98.3%、60.0%和 25.0%,而在 CECT/MRI 中则分别为 95.9%、65.7%和 48.1%。LR-5 诊断 HCC 的灵敏度和特异度分别为 CEUS 的 75.6%和 93.8%,CECT/MRI 的 83.6%和 83.3%。CEUS LR-M 和 CECT/MRI LR-M 中诊断非 HCC 恶性肿瘤的阳性预测值分别为 33.9%和 93.3%。CEUS LR-M 诊断非 HCC 恶性肿瘤的灵敏度、特异度和准确度分别为 90.9%、84.5%和 85.0%,而 CECT/MRI LR-M 则分别为 63.6%、99.6%和 96.7%。

结论

CEUS 和 CECT/MRI 的 LI-RADS 分类间的一致性为中等。CEUS 和 CECT/MRI LI-RADS 的 LR-5 对 HCC 的阳性预测值相当。CECT/MRI LR-M 对非 HCC 恶性肿瘤的诊断性能优于 CEUS LR-M。

关键要点

• CEUS 和 CECT/MRI 的最终 LI-RADS 分类间的一致性为中等。

• CEUS 和 CECT/MRI LI-RADS 的 LR-5 对应 HCC 的阳性预测值(PPV)相当。对于 CECT/MRI 分类为 LR-3 和 LR-4 的结节,如果能在普通超声上检测到,应进行 CEUS 检查。

• CECT/MRI LR-M 对非 HCC 恶性肿瘤的诊断性能优于 CEUS LR-M。对于 CEUS 分类为 LR-M 的结节,需要再次进行 CECT/MRI 评估。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验