Zhu Yadi, Yang Ling, Shen Hailin
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China.
Front Oncol. 2021 Nov 19;11:757111. doi: 10.3389/fonc.2021.757111. eCollection 2021.
To explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.
The clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (=123) and validation set (= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.
There is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.
We revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.
探讨基于对比增强磁共振成像（CE-MRI）影像组学特征的机器学习模型在乳腺癌前哨淋巴结（SLN）转移术前预测中的价值。
回顾性分析2015年1月至2021年5月在苏州大学附属第一医院接受手术前常规DCE-MRI检查且病理确诊为乳腺癌的177例患者的临床、病理及MRI数据（81例SLN阳性，96例SLN阴性）。样本按7:3的比例随机分为训练集（=123）和验证集（=54）。影像组学特征来源于DCE-MRI的第2期图像，1316个原始特征向量经最大最小归一化处理。采用最优特征筛选和选择算子（LASSO）算法获取最优特征。基于所选特征构建支持向量机、随机森林、逻辑回归、梯度提升决策树和决策树5种机器学习模型。将影像组学特征和独立危险因素纳入构建联合模型。采用受试者工作特征曲线及曲线下面积评估上述模型的性能，并计算准确性、敏感性和特异性。
乳腺癌患者中，有无SLN转移的所有临床和组织病理学变量之间均无显著差异（P>0.05），除肿瘤大小和BI-RADS分类外（P<0.01）。获得13个特征作为构建机器学习模型的最优特征。在验证集中，支持向量机的曲线下面积（AUC，0.86）在5种机器学习模型中最高。同时，联合模型在预测前哨淋巴结转移（SLNM）方面表现更佳，在验证集中达到了更高的AUC（0.88）。
我们揭示了基于CE-MRI影像组学特征建立的机器学习模型的临床价值，为乳腺癌患者SLNM的术前预测提供了一种高度准确、无创且便捷的方法。
