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基于合成弛豫率和扩散测量对乳腺良恶性病变的鉴别诊断与 BI-RADS 比较的研究。

Investigation of Synthetic Relaxometry and Diffusion Measures in the Differentiation of Benign and Malignant Breast Lesions as Compared to BI-RADS.

机构信息

Department of Radiology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Department of Oncology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

出版信息

J Magn Reson Imaging. 2021 Apr;53(4):1118-1127. doi: 10.1002/jmri.27435. Epub 2020 Nov 12.

Abstract

BACKGROUND

Breast cancer is the most common malignant tumor in women and a quantitative contrast-free method is highly desirable for its diagnosis.

PURPOSE

To investigate the performance of quantitative MRI in differentiating malignant from benign breast lesions and to compare with the Breast Imaging Reporting and Data System (BI-RADS).

STUDY TYPE

Retrospective.

SUBJECTS

Eighty patients (56 with malignant lesions and 24 with benign lesions).

FIELD STRENGTH/SEQUENCE: Diffusion-weighted imaging (DWI) with a single-shot echo planar sequence and synthetic MRI with magnetic resonance image compilation (MAGiC) were performed at 3T.

ASSESSMENT

T relaxation time (T ), T relaxation time (T ), and proton density (PD) from synthetic MRI and apparent diffusion coefficient (ADC) from DWI were analyzed by two radiologists (Reader A, Reader B). Univariable and multivariable models were developed to optimize differentiation between malignant and benign lesions and their performances compared to BI-RADS.

STATISTICAL TESTS

The diagnostic performance was evaluated using multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curves (AUC).

RESULTS

T , PD, and ADC values for malignant lesions were significantly lower than those in benign breast lesions for both radiologists (all P < 0.05). The combined T , PD, and ADC model had the best performance for differentiating malignant and benign lesions with AUC, sensitivity, specificity, positive predictive value, and negative predictive values of 0.904, 94.6%, 87.5%, 94.6%, and 87.5%, respectively. The corresponding results for BI-RADS were no AUC, 94.6%, 75.0%, 89.8%, and 85.7%, respectively.

DATA CONCLUSION

The approach that combined synthetic MRI and DWI outperformed BI-RADS in the differential diagnosis of malignant and benign breast lesions and was achieved without contrast agents. This approach may serve as an alternative and effective strategy for the improvement of breast lesion differentiation.

LEVEL OF EVIDENCE

TECHNICAL EFFICACY STAGE

摘要

背景

乳腺癌是女性最常见的恶性肿瘤,因此非常需要一种定量的无对比方法进行诊断。

目的

探讨定量 MRI 鉴别良恶性乳腺病变的性能,并与乳腺影像报告和数据系统(BI-RADS)进行比较。

研究类型

回顾性。

受试者

80 例患者(56 例恶性病变,24 例良性病变)。

磁场强度/序列:在 3T 上进行单次激发回波平面序列的弥散加权成像(DWI)和磁共振图像合成(MAGiC)的合成 MRI。

评估

由两位放射科医生(Reader A、Reader B)分析合成 MRI 的 T1 弛豫时间(T1)、T2 弛豫时间(T2)和质子密度(PD)以及 DWI 的表观扩散系数(ADC)。建立单变量和多变量模型以优化良恶性病变的鉴别,并与 BI-RADS 进行比较。

统计学检验

使用多变量逻辑回归分析和受试者工作特征(ROC)曲线下面积(AUC)评估诊断性能。

结果

两位放射科医生均发现恶性病变的 T1、PD 和 ADC 值明显低于良性乳腺病变(均 P < 0.05)。对于区分良恶性病变,T1、PD 和 ADC 的联合模型具有最佳性能,其 AUC、敏感度、特异度、阳性预测值和阴性预测值分别为 0.904、94.6%、87.5%、94.6%和 87.5%。BI-RADS 的相应结果为无 AUC、94.6%、75.0%、89.8%和 85.7%。

数据结论

联合合成 MRI 和 DWI 的方法在良恶性乳腺病变的鉴别诊断中优于 BI-RADS,且无需使用造影剂。该方法可能是改善乳腺病变鉴别诊断的一种替代且有效的策略。

证据水平

3

技术功效分级

3

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