Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Roszkowska-Purska K, Nowicki A, Jakubowski W
Department of Radiology, Cancer Center and Institute of Oncology M. Sklodowska-Curie Memorial, Warsaw, Poland; Institute of Fundamental Technological Research, PAS, Warsaw, Poland.
Institute of Fundamental Technological Research, PAS, Warsaw, Poland.
Clin Radiol. 2017 Apr;72(4):339.e7-339.e15. doi: 10.1016/j.crad.2016.11.009. Epub 2016 Dec 27.
To develop a method combining the statistics of the ultrasound backscatter and the Breast Imaging-Reporting and Data System (BI-RADS) classification to enhance the differentiation of breast tumours.
The Nakagami shape parameter m was used to characterise the scatter properties of breast tumours. Raw data from the radiofrequency (RF) echo-signal and B-mode images from 107 (32 malignant and 75 benign) lesions and their surrounding tissue were recorded. Three different characteristic values of the shape parameters of m (maximum [mLmax], minimum [mLmin] and average [mLavg]) and differences between m parameters (Δmmax, Δmmin, Δmavg) of the lesions and their surrounding tissues were assessed. A lesion with a BI-RADS score of 3 was considered benign, while a lesion with a score of 4 was considered malignant (a cut-off of BI-RADS 3/4 was set for all patients).
The area under the receiver operating characteristic (ROC) curve (AUC) was equal to 0.966 for BI-RADS, with 100% sensitivity and 54.67% specificity. All malignant lesions were diagnosed correctly, whereas 34 benign lesions were biopsied unnecessarily. In assessing the Nakagami statistics, the sum of the sensitivity and specificity was the best for mLavg (62.5% and 93.33%, respectively). Only four of 20 lesions were found over the cut-off value in BI-RADS of 4a. When comparing the differences in m parameters, Δmavg had the highest sensitivity of 90% (only three of 32 lesions were false negative). These three lesions were classified as BI-RADS category 4c. The combined use of B-mode and mLmin parameter improve the AUC up to 0.978 (p=0.088), compared to BI-RADS alone.
The combination of the parametric imaging and the BI-RADS assessment does not significantly improve the differentiation of breast lesions, but it has the potential to better identify the group of patients with mainly benign lesions that have a low level of suspicion for malignancy with a BI-RADS score of 4a.
开发一种将超声背散射统计数据与乳腺影像报告和数据系统（BI-RADS）分类相结合的方法，以提高乳腺肿瘤的鉴别能力。
采用 Nakagami 形状参数 m 来表征乳腺肿瘤的散射特性。记录了 107 个病变（32 个恶性和 75 个良性）及其周围组织的射频（RF）回波信号和 B 模式图像的原始数据。评估了病变及其周围组织的 m 形状参数的三个不同特征值（最大值[mLmax]、最小值[mLmin]和平均值[mLavg]）以及 m 参数之间的差异（Δmmax、Δmmin、Δmavg）。BI-RADS 评分为 3 的病变被视为良性，而评分为 4 的病变被视为恶性（为所有患者设定 BI-RADS 3/4 的临界值）。
BI-RADS 的受试者操作特征（ROC）曲线下面积（AUC）等于 0.966，敏感性为 100%，特异性为 54.67%。所有恶性病变均被正确诊断，而 34 个良性病变被不必要地进行了活检。在评估 Nakagami 统计数据时，mLavg 的敏感性和特异性之和最佳（分别为 62.5%和 93.33%）。在 BI-RADS 4a 中，20 个病变中只有 4 个超过临界值。比较 m 参数的差异时，Δmavg 的敏感性最高，为 90%（32 个病变中只有 3 个假阴性）。这三个病变被分类为 BI-RADS 4c 类。与单独使用 BI-RADS 相比，B 模式和 mLmin 参数的联合使用将 AUC 提高到 0.978（p = 0.088）。
参数成像与 BI-RADS 评估的结合并未显著提高乳腺病变的鉴别能力，但它有可能更好地识别 BI-RADS 评分为 4a、恶性怀疑程度较低且主要为良性病变的患者群体。
