Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, Korea.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Comput Methods Programs Biomed. 2017 Jul;146:143-150. doi: 10.1016/j.cmpb.2017.06.001. Epub 2017 Jun 3.
The presence or absence of axillary lymph node (ALN) metastasis is the most important prognostic factor for patients with early-stage breast cancer. In this study, a computer-aided prediction (CAP) system using the tumor surrounding tissue features in ultrasound (US) images was proposed to determine the ALN status in breast cancer.
The US imaging database used in this study contained 114 cases of invasive breast cancer and 49 of them were ALN metastasis. After the tumor region segmentation by the level set method, image matting method was used to extract surrounding abnormal tissue of tumor from the acquired images. Then, 21 features composed of 2 intensity, 3 morphology, and 16 textural features are extracted from the surrounding tissue and processed by a logistic regression model. Finally, the prediction model is trained and tested from the selected features.
In the experiments, the textural feature set extracted from surrounding tissue showed higher performance than intensity and morphology feature sets (Az, 0.7756 vs 0.7071 and 0.6431). The accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic (ROC) curve for the CAP system were 81.58% (93/114), 81.63% (40/49), 81.54% (53/65), and 0.8269 for using combined feature set.
These results indicated that the proposed CAP system can be helpful to determine the ALN status in patients with breast cancer.
腋窝淋巴结（ALN）转移的存在与否是早期乳腺癌患者最重要的预后因素。本研究提出了一种基于肿瘤周围组织超声（US）图像特征的计算机辅助预测（CAP）系统，用于确定乳腺癌的 ALN 状态。
本研究使用的 US 成像数据库包含 114 例浸润性乳腺癌病例，其中 49 例为 ALN 转移。通过水平集方法进行肿瘤区域分割后，使用图像遮罩方法从获取的图像中提取肿瘤周围异常组织。然后，从周围组织中提取由 2 个强度、3 个形态和 16 个纹理特征组成的 21 个特征，并通过逻辑回归模型进行处理。最后，从选择的特征中训练和测试预测模型。
在实验中，从周围组织中提取的纹理特征集的性能优于强度和形态特征集（Az 值分别为 0.7756、0.7071 和 0.6431）。该 CAP 系统的准确率、敏感度、特异度和受试者工作特征（ROC）曲线下的面积指数 Az 分别为 81.58%（93/114）、81.63%（40/49）、81.54%（53/65）和 0.8269。
这些结果表明，所提出的 CAP 系统有助于确定乳腺癌患者的 ALN 状态。
