Zhao Binghui, Zhang Xiaohua, Cai Weixing, Conover David, Ning Ruola
Department of Radiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China.
Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA.
Eur J Radiol. 2015 Jan;84(1):48-53. doi: 10.1016/j.ejrad.2014.05.032. Epub 2014 Jul 15.
This pilot study was to evaluate cone beam breast computed tomography (CBBCT) with multiplanar and three dimensional (3D) visualization in differentiating breast masses in comparison with two-view mammograms.
Sixty-five consecutive female patients (67 breasts) were scanned by CBBCT after conventional two-view mammography (Hologic, Motarget, compression factor 0.8). For CBBCT imaging, three hundred (1024 × 768 × 16b) two-dimensional (2D) projection images were acquired by rotating the x-ray tube and a flat panel detector (FPD) 360 degree around one breast. Three-dimensional CBBCT images were reconstructed from the 2D projections. Visage CS 3.0 and Amira 5.2.2 were used to visualize reconstructed CBBCT images.
Eighty-five breast masses in this study were evaluated and categorized under the breast imaging reporting and data system (BI-RADS) according to plain CBBCT images and two-view mammograms, respectively, prior to biopsy. BI-RADS category of each breast was compared with biopsy histopathology. The results showed that CBBCT with multiplanar and 3D visualization would be helpful to identify the margin and characteristics of breast masses. The category variance ratios for CBBCT under the BI-RADS were 23.5% for malignant tumors (MTs) and 27.3% for benign lesions in comparison with pathology, which were evidently closer to the histopathology results than those of two-view mammograms, p value <0.01. With the receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) of CBBCT was 0.911, larger than that (AUC 0.827) of two-view mammograms, p value <0.01.
CBBCT will be a distinctive noninvasive technology in differentiating and categorizing breast masses under BI-RADS. CBBCT may be considerably more effective to identify breast masses, especially some small, uncertain or multifocal masses than conventional two-view mammography.
本初步研究旨在评估锥束乳腺计算机断层扫描（CBBCT）在多平面和三维（3D）可视化方面与双视图乳腺X线摄影相比对乳腺肿块的鉴别能力。
65例连续女性患者（67个乳房）在进行传统双视图乳腺X线摄影（Hologic，Motarget，压迫因子0.8）后接受CBBCT扫描。对于CBBCT成像，通过围绕一个乳房将X射线管和平板探测器（FPD）旋转360度获取300幅（1024×768×16b）二维（2D）投影图像。从2D投影重建三维CBBCT图像。使用Visage CS 3.0和Amira 5.2.2对重建的CBBCT图像进行可视化。
本研究中的85个乳腺肿块在活检前分别根据CBBCT平扫图像和双视图乳腺X线摄影按照乳腺影像报告和数据系统（BI-RADS）进行评估和分类。将每个乳房的BI-RADS类别与活检组织病理学进行比较。结果表明，具有多平面和3D可视化的CBBCT有助于识别乳腺肿块的边缘和特征。与病理学相比，CBBCT在BI-RADS下的类别差异率对于恶性肿瘤（MTs）为23.5%，对于良性病变为27.3%，明显比双视图乳腺X线摄影更接近组织病理学结果，p值<0.01。通过受试者操作特征（ROC）曲线分析，CBBCT的曲线下面积（AUC）为0.911，大于双视图乳腺X线摄影的（AUC 0.827），p值<0.01。
CBBCT将是一种在BI-RADS下对乳腺肿块进行鉴别和分类的独特无创技术。与传统双视图乳腺X线摄影相比，CBBCT在识别乳腺肿块方面可能更有效，尤其是一些小的、不确定的或多灶性肿块。
