Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, No 270, Dong'an Road, Xuhui District, Shanghai, 200032, China.
Eur Radiol. 2022 Mar;32(3):1590-1600. doi: 10.1007/s00330-021-08224-x. Epub 2021 Sep 14.
Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment.
We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients. All patients were subgrouped according to the histological grade, Ki67 expression level, and human epidermal growth factor receptor 2 (HER2) score. Qualitative sonographic feature assessment included shape, margin, posterior acoustic pattern, and calcification referring to the Breast Imaging Reporting and Data System (BI-RADS). Quantitative sonographic features were acquired based on the computer-aided radiomics analysis. Breast cancer masses were manually segmented from the surrounding breast tissues. For each ultrasound image, 1688 radiomics features of 7 feature classes were extracted. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were used to determine the high-throughput radiomics features that were highly correlated to biological properties. The performance using both quantitative and qualitative sonographic features to predict biological properties of TNBC was represented by the area under the receiver operating characteristic curve (AUC).
In the qualitative assessment, regular tumor shape, no angular or spiculated margin, posterior acoustic enhancement, and no calcification were used as the independent sonographic features for TNBC. Using the combination of these four features to predict the histological grade, Ki67, HER2, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI), the AUC was 0.673, 0.680, 0.651, 0.587, and 0.566, respectively. The number of high-throughput features that closely correlated with biological properties was 34 for histological grade (AUC 0.942), 27 for Ki67 (AUC 0.732), 25 for HER2 (AUC 0.730), 34 for ALNM (AUC 0.804), and 34 for LVI (AUC 0.795).
High-throughput quantitative sonographic features are superior to traditional qualitative ultrasound features in predicting the biological behavior of TNBC.
• Sonographic appearances of TNBCs showed a great variety in accordance with its biological and clinical characteristics. • Both qualitative and quantitative sonographic features of TNBCs are associated with tumor biological characteristics. • The quantitative high-throughput feature analysis is superior to two-dimensional sonographic feature assessment in predicting tumor biological property.
超声特征与三阴性乳腺癌（TNBC）的病理和免疫组织化学特征相关。为了预测 TNBC 的生物学特性，我们比较了使用定量高通量超声特征分析与定性特征评估的性能。
我们回顾性分析了 252 例女性 TNBC 患者的超声图像、临床、病理和免疫组织化学（IHC）数据。所有患者根据组织学分级、Ki67 表达水平和人表皮生长因子受体 2（HER2）评分进行分组。定性超声特征评估包括形状、边界、后向声影模式和钙化，参照乳腺影像报告和数据系统（BI-RADS）。定量超声特征基于计算机辅助放射组学分析获得。从周围乳腺组织手动分割乳腺癌肿块。对于每个超声图像，提取 7 个特征类别的 1688 个放射组学特征。主成分分析（PCA）、最小绝对值收缩和选择算子（LASSO）和支持向量机（SVM）用于确定与生物学特性高度相关的高通量放射组学特征。使用定量和定性超声特征来预测 TNBC 生物学特性的性能由受试者工作特征曲线下面积（AUC）表示。
在定性评估中，规则的肿瘤形状、无角或分叶状边界、后向声增强和无钙化被用作 TNBC 的独立超声特征。使用这四个特征的组合来预测组织学分级、Ki67、HER2、腋窝淋巴结转移（ALNM）和脉管侵犯（LVI），AUC 分别为 0.673、0.680、0.651、0.587 和 0.566。与生物学特性密切相关的高通量特征数量为组织学分级 34 个（AUC 0.942）、Ki67 27 个（AUC 0.732）、HER2 25 个（AUC 0.730）、ALNM 34 个（AUC 0.804）和 LVI 34 个（AUC 0.795）。
高通量定量超声特征优于传统二维超声特征，可预测 TNBC 的生物学行为。
• TNBC 的超声表现与其生物学和临床特征一致，表现出多样性。• TNBC 的定性和定量超声特征均与肿瘤生物学特征相关。• 高通量特征分析优于二维超声特征评估，可预测肿瘤生物学特性。
