Yao J P, Niu L J, Wang Y, Geng C Y, Chang Q, Chen Y, Zhu L
Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Zhonghua Zhong Liu Za Zhi. 2018 Sep 23;40(9):672-675. doi: 10.3760/cma.j.issn.0253-3766.2018.09.006.
To analyze the feature of breast complex cystic masses and to classify it at ultrasonography (US), which applied to the Breast Imaging Reporting and Data System (BI-RADS) categories 4a to 4c with pathological results as the golden standards. The ultrasonographic data and clinical features of 78 patients with complex cystic masses confirmed by pathology in Cancer Hospital from July 2014 to June 2017 were retrospectively reviewed. The complex cystic breast masses were divided into four classes on the basis of their US features: type 1 [thick wall and (or) thick septa (> 0.5 mm)], type 2 (one or more intra-cystic masses), type 3 (mixed cystic and solid components with cystic components more than 50%) and type 4 (mixed cystic and solid components with solid components more than 50%). Positive values (PPVs) were calculated for each type. Multiple linear regression analysis was used to analyze the ultrasonographic features of the masses (lesion size, margins, blood flow resistance index, calcification, and axillary lymph nodes, etc.) with malignant correlation. There were 81 lesions in 78 patients. Among the 81 masses based on US appearance, 14 (17.3%) were classified as type Ⅰ, 18 (22.2%) as type Ⅱ, 18 (22.2%) as type Ⅲ, and 31 (38.3%) as type Ⅳ. The positive predictive values of the malignant lesions of type Ⅰ, type Ⅱ, Ⅲ and Ⅳ were 7.1%, 16.7%, 61.1% and 48.3%, respectively (=0.040). In all the 81 masses, 14 were BI-RADS categories 4a, 18 were BI-RADS categories 4b and 49 were BI-RADS categories 4c. Masses with maximum diameter equal to or larger than 2.0 cm, unclear margins, RI≥0.7 and presence of abnormal axillary nodes assessment had a high probability of malignancy (=0.030, 0.038, <0.001 and 0.025, respectively). Ultrasound typing is helpful for differentiating benign and malignant breast complex cysts and classifying BI-AIDS 4a to 4c, thus providing clearer treatment for clinical practice.
以病理结果为金标准,分析乳腺复杂性囊性肿块的特征并在超声检查(US)下对其进行分类,这些肿块应用于乳腺影像报告和数据系统(BI-RADS)4a至4c类。回顾性分析2014年7月至2017年6月在癌症医院经病理证实的78例复杂性囊性肿块患者的超声数据和临床特征。根据超声特征将复杂性囊性乳腺肿块分为四类:1型[厚壁和(或)厚分隔(>0.5mm)],2型(一个或多个囊内肿块),3型(囊性和实性成分混合,囊性成分超过50%)和4型(囊性和实性成分混合,实性成分超过50%)。计算每种类型的阳性预测值(PPV)。采用多元线性回归分析肿块的超声特征(病变大小、边界、血流阻力指数、钙化和腋窝淋巴结等)与恶性相关性。78例患者共有81个病灶。在81个基于超声表现的肿块中,14个(17.3%)分类为Ⅰ型,18个(22.2%)为Ⅱ型,18个(22.2%)为Ⅲ型,31个(38.3%)为Ⅳ型。Ⅰ型、Ⅱ型、Ⅲ型和Ⅳ型恶性病变的阳性预测值分别为7.1%、16.7%、61.1%和48.3%(P=0.040)。在所有81个肿块中,14个为BI-RADS 4a类,18个为BI-RADS 4b类,49个为BI-RADS 4c类。最大直径等于或大于2.0cm、边界不清、RI≥0.7以及存在异常腋窝淋巴结评估的肿块具有较高的恶性概率(P分别为0.030、0.038、<0.001和0.025)。超声分型有助于鉴别乳腺复杂性囊肿的良恶性并对BI-AIDS 4a至4c进行分类,从而为临床实践提供更清晰的治疗依据。