Institute of Biomedical Engineering, Shanghai University, Shanghai, China; Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, China.
Institute of Biomedical Engineering, Shanghai University, Shanghai, China.
Eur J Radiol. 2017 Oct;95:66-74. doi: 10.1016/j.ejrad.2017.07.027. Epub 2017 Aug 1.
To propose a computer-assisted method for quantifying the hardness of an axillary lymph node on real-time elastography (RTE) and its morphology on B-mode ultrasound; and to combine the dual-modal features for differentiation of metastatic and benign axillary lymph nodes in breast cancer patients.
A total of 161 axillary lymph nodes (benign, n=69; metastatic, n=92) from 158 patients with breast cancer were examined with both B-mode ultrasound and RTE. With computer assistance, five morphological features describing the hilum, size, shape, and echogenic uniformity of a lymph node were extracted from B-mode, and three hardness features were extracted from RTE. Single-modal and dual-modal features were used to classify benign and metastatic nodes with two computerized classification approaches, i.e., a scoring approach and a support vector machine (SVM) approach. The computerized approaches were also compared with a visual evaluation approach.
All features exhibited significant differences between benign and metastatic nodes (p<0.001), with the highest area under the receiver operating characteristic curve (AUC) of 0.803 and the highest accuracy (ACC) of 75.2% for a single feature. The SVM on dual-modal features achieved the largest AUC (0.895) and ACC (85.7%) among all methods, exceeding the scoring (AUC=0.881; ACC=83.6%) and the visual evaluation methods (AUC=0.830; ACC=84.5%). With the leave-one-out cross validation, the SVM on dual-modal features still obtained an ACC as high as 84.5%.
Dual-modal features can be extracted from RTE and B-mode ultrasound with computer assistance, which are valuable for discrimination between benign and metastatic lymph nodes. The SVM on dual-modal features outperforms the scoring and visual evaluation methods, as well as all methods using single-modal features. The computer-assisted dual-modal evaluation of lymph nodes could be potentially used in daily clinical practice for assessing axillary metastasis in breast cancer patients.
提出一种基于实时超声弹性成像（RTE）和 B 型超声的计算机辅助方法，对腋窝淋巴结的硬度进行量化，并对其形态进行分析；结合双模态特征，区分乳腺癌患者腋窝转移性和良性淋巴结。
对 158 例乳腺癌患者的 161 个腋窝淋巴结（良性 69 个，转移性 92 个）同时进行 B 型超声和 RTE 检查。借助计算机辅助，从 B 型超声中提取描述淋巴结门、大小、形状和回声均匀性的 5 个形态学特征，从 RTE 中提取 3 个硬度特征。使用两种计算机分类方法（评分法和支持向量机（SVM）法），对单模态和双模态特征进行良性和转移性淋巴结的分类。并将计算机方法与视觉评估方法进行比较。
所有特征在良性和转移性淋巴结之间均有显著差异（p<0.001），其中单个特征的曲线下面积（AUC）最大为 0.803，准确率（ACC）最高为 75.2%。双模态特征的 SVM 获得了所有方法中最大的 AUC（0.895）和 ACC（85.7%），优于评分法（AUC=0.881；ACC=83.6%）和视觉评估方法（AUC=0.830；ACC=84.5%）。在留一法交叉验证中，SVM 对双模态特征的 ACC 仍高达 84.5%。
借助计算机辅助，可从 RTE 和 B 型超声中提取双模态特征，这些特征对鉴别良性和转移性淋巴结具有重要价值。SVM 对双模态特征的分类优于评分法和视觉评估方法，也优于所有单模态特征的方法。计算机辅助的双模态淋巴结评估可能在乳腺癌患者腋窝转移的日常临床实践中具有潜在应用价值。
