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Abstract：
This study aimed to assess the utility of second-look ultrasonography (US) in differentiating breast imaging reporting and data system (BI-RADS) 4 calcifications initially detected on mammography (MG). BI-RADS 4 calcifications have a wide range of positive predictive values. We hypothesized that second-look US would help distinguish BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG. This study included 1622 pure BI-RADS 4 calcifications in 1510 women (112 patients with bilateral calcifications). The cases were randomly divided into training (85%) and testing (15%) datasets. Two nomograms were developed to differentiate BI-RADS 4 calcifications in the training dataset: the MG-US nomogram, based on multifactorial logistic regression and incorporated clinical information, MG, and second-look US characteristics, and the MG nomogram, based on clinical information and mammographic characteristics. Calibration of the MG-US nomogram was performed using calibration curves. The discriminative ability and clinical utility of both nomograms were compared using the area under the receiver operating characteristic curve (AUC) and the decision analysis curve (DCA) in the test dataset. The clinical information and imaging characteristics were comparable between the training and test datasets. The bias-corrected calibration curves of the MG-US nomogram closely approximate the ideal line for both datasets. In the test dataset, the MG-US nomogram exhibited a higher AUC than the MG nomogram (0.899 vs 0.852, P = .01). DCA demonstrated the superiority of the MG-US nomogram over the MG nomogram. Second-look US features, including ultrasonic calcifications, lesions, and moderate or marked color flow, were valuable for distinguishing BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG.
摘要：
这项研究旨在评估二次超声检查（US）在区分乳腺成像报告和数据系统（BI-RADS）4个最初在乳腺X线摄影（MG）上检测到的钙化中的实用性。BI-RADS4钙化具有广泛的阳性预测值。我们假设第二外观US将有助于区分BI-RADS4钙化，而没有MG的临床表现和其他异常。这项研究包括1510名女性（112例双侧钙化患者）的1622例纯BI-RADS4钙化。这些病例被随机分为训练(85%)和测试(15%)数据集。开发了两个列线图来区分训练数据集中的BI-RADS4钙化：MG-US列线图，基于多因素逻辑回归和整合的临床信息，MG,和第二看美国的特点，和MG列线图，基于临床信息和乳房X线特征。使用校准曲线进行MG-US列线图的校准。使用测试数据集中的受试者工作特征曲线（AUC）和决策分析曲线（DCA）下的面积比较了两个列线图的判别能力和临床实用性。训练和测试数据集之间的临床信息和成像特征具有可比性。MG-US列线图的偏差校正校准曲线非常接近两个数据集的理想线。在测试数据集中，MG-US列线图的AUC高于MG列线图(0.899vs0.852,P=.01).DCA证明了MG-US列线图优于MG列线图。第二看美国的特点，包括超声钙化,病变,和中等或标记的颜色流，对区分MG无临床表现和其他异常的BI-RADS4钙化有价值。
