Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Comput Methods Programs Biomed. 2018 Aug;162:129-137. doi: 10.1016/j.cmpb.2018.05.011. Epub 2018 May 16.
Axillary lymph node (ALN) status is a key indicator in assessing and determining the treatment strategy for patients with newly diagnosed breast cancer. Previous studies suggest that sonographic features of a primary tumor have the potential to predict ALN status in the preoperative staging of breast cancer. In this study, a computer-aided prediction (CAP) model as well as the tumor features for ALN metastasis in breast cancers were developed using breast ultrasound (US) images.
A total of 249 malignant tumors were acquired from 247 female patients (ages 20-84 years; mean 55 ± 11 years) to test the differences between the non-metastatic (130) and metastatic (119) groups based on various features. After applying semi-automatic tumor segmentation, 69 quantitative features were extracted. The features included morphology and texture of tumors inside a ROI of breast US image. By the backward feature selection and linear logistic regression, the prediction model was constructed and established to estimate the likelihood of ALN metastasis for each sample collected.
In the experiments, the texture features showed higher performance for predicting ALN metastasis compared to morphology (Az, 0.730 vs 0.667). The difference, however, was not statistically significant (p-values > 0.05). Combining the textural and morphological features, the accuracy, sensitivity, specificity, and Az value achieved 75.1% (187/249), 79.0% (94/119), 71.5% (93/130), and 0.757, respectively.
The proposed CAP model, which combines textural and morphological features of primary tumor, may be a useful method to determine the ALN status in patients with breast cancer.
腋窝淋巴结（ALN）状态是评估和确定新诊断乳腺癌患者治疗策略的关键指标。先前的研究表明，原发性肿瘤的超声特征有可能预测乳腺癌术前分期的 ALN 状态。在这项研究中，我们使用乳腺超声（US）图像开发了一种计算机辅助预测（CAP）模型以及乳腺癌中肿瘤特征与 ALN 转移的关系。
共从 247 名女性患者（年龄 20-84 岁；平均 55±11 岁）中获得 249 个恶性肿瘤，以根据各种特征比较非转移性（130 个）和转移性（119 个）组之间的差异。在应用半自动肿瘤分割后，提取了 69 个定量特征。这些特征包括乳腺 US 图像 ROI 内肿瘤的形态和纹理。通过后向特征选择和线性逻辑回归，构建并建立预测模型以估计每个样本的 ALN 转移可能性。
在实验中，与形态相比，纹理特征在预测 ALN 转移方面表现出更高的性能（Az 值分别为 0.730 和 0.667）。但是，差异没有统计学意义（p 值均>0.05）。结合纹理和形态特征，准确率、敏感度、特异度和 Az 值分别达到 75.1%（187/249）、79.0%（94/119）、71.5%（93/130）和 0.757。
该研究提出的 CAP 模型结合了原发性肿瘤的纹理和形态特征，可能是一种确定乳腺癌患者 ALN 状态的有用方法。
