From the Department of Radiology, Breast Imaging Center (I.T., M.E.K.), Department of Surgical Oncology, Breast Care Center (A.R.), and Department of Pathology (L.G.), Centre Hospitalier de l'Université de Montréal, 3840 Saint-Urbain, Montreal, QC, Canada H2W 1T8; Department of Radiology, Radio-Oncology and Nuclear Medicine (I.T., M.E.K., G.C.) and Institute of Biomedical Engineering (G.C.), Université de Montréal, Montreal, Quebec, Canada; and Laboratory of Biorheology and Medical Ultrasonics, Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Quebec, Canada (F.D., L.A., B.C., G.C.).
Radiology. 2015 Jun;275(3):666-74. doi: 10.1148/radiol.14140318. Epub 2014 Dec 12.
To develop a classification method based on the statistical backscatter properties of tissues that can be used as an ancillary tool to the usual Breast Imaging Reporting and Data System (BI-RADS) classification for solid breast lesions identified at ultrasonography (US).
This study received institutional review board approval, and all subjects provided informed consent. Eighty-nine women (mean age, 50 years; age range, 22-82 years) with 96 indeterminate solid breast lesions (BI-RADS category 4-5; mean size, 13.2 mm; range, 2.6-44.7 mm) were enrolled. Prior to biopsy, additional radiofrequency US images were obtained, and a 3-second cine sequence was used. The research data were analyzed at a later time and were not used to modify patient management decisions. The lesions were segmented manually, and parameters of the homodyned K distribution (α, k, and μn values) were extracted for three regions: the intratumoral zone, a 3-mm supratumoral zone, and a 5-mm infratumoral zone. The Mann-Whitney rank sum test was used to identify parameters with the best discriminating value, yielding intratumoral α, supratumoral k, and infratumoral μn values.
The 96 lesions were classified as follows: 48 BI-RADS category 4A lesions, 16 BI-RADS category 4B lesions, seven BI-RADS category 4C lesions, and 25 BI-RADS category 5 lesions. There were 24 cancers (25%). The area under the receiver operating characteristic curve was 0.76 (95% confidence interval: 0.65, 0.86). Overall, 24% of biopsies (in 17 of 72 lesions) could have been spared. By limiting analysis to lesions with a lower likelihood of malignancy (BI-RADS category 4A-4B), this percentage increased to 26% (16 of 62 lesions). Among benign lesions, the model was used to correctly classify 10 of 38 fibroadenomas (26%) and three of seven stromal fibroses (43%).
The statistical model performs well in the classification of solid breast lesions at US, with the potential of preventing one in four biopsies without missing any malignancy.
开发一种基于组织统计反向散射特性的分类方法,可作为超声(US)检查中发现的实性乳腺病变通常采用的乳腺影像报告和数据系统(BI-RADS)分类的辅助工具。
本研究获得了机构审查委员会的批准,所有受试者均提供了知情同意书。共纳入 89 名女性(平均年龄 50 岁;年龄范围:22-82 岁),共 96 个不定性实性乳腺病变(BI-RADS 类别 4-5;平均大小 13.2mm;范围:2.6-44.7mm)。在活检之前,还获取了额外的射频 US 图像,并使用 3 秒的电影序列。之后在更晚的时间对研究数据进行分析,且该分析结果并未用于修改患者的管理决策。手动对病变进行分割,并提取同调 K 分布(α、k 和μn 值)的三个区域(肿瘤内区、肿瘤旁 3mm 区和肿瘤下 5mm 区)的参数。采用 Mann-Whitney 秩和检验来识别具有最佳判别价值的参数,得到肿瘤内α、肿瘤旁 k 和肿瘤下μn 值。
96 个病变分类如下:48 个 BI-RADS 类别 4A 病变、16 个 BI-RADS 类别 4B 病变、7 个 BI-RADS 类别 4C 病变和 25 个 BI-RADS 类别 5 病变。其中有 24 个癌症(25%)。受试者工作特征曲线下面积为 0.76(95%置信区间:0.65,0.86)。总体而言,24%的活检(72 个病变中的 17 个)可以避免。通过将分析仅限于恶性可能性较低的病变(BI-RADS 类别 4A-4B),该比例增加到 26%(62 个病变中的 16 个)。在良性病变中,该模型正确分类了 10 个纤维腺瘤(26%)和 7 个间质纤维化中的 3 个(43%)。
统计模型在超声检查实性乳腺病变的分类中表现良好,有潜力在不遗漏任何恶性肿瘤的情况下,避免四分之一的活检。