PDF(Pubmed)
Abstract：
To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging (DCE-MRI) radiomics and to explore their potential prognostic value in the differential diagnosis of human epidermal growth factor receptor 2 (HER2)-low from HER2-positive breast cancer (BC). A total of 233 patients with pathologically confirmed invasive breast cancer admitted to our hospital between January 2018 and December 2022 were included in this retrospective analysis. Of these, 103 cases were diagnosed as HER2-positive and 130 cases were HER2 low-expression BC. The Synthetic Minority Oversampling Technique is employed to address the class imbalance problem. Patients were randomly split into a training set (163 cases) and a validation set (70 cases) in a 7:3 ratio. Radiomics features from DCE-MRI second-phase imaging were extracted. Z-score normalization was used to standardize the radiomics features, and Pearson\'s correlation coefficient and recursive feature elimination were used to explore the significant features. Prediction models were constructed using 6 machine learning algorithms: logistic regression, random forest, support vector machine, AdaBoost, decision tree, and auto-encoder. Receiver operating characteristic curves were constructed, and predictive models were evaluated according to the area under the curve (AUC), accuracy, sensitivity, and specificity. In the training set, the AUC, accuracy, sensitivity, and specificity of all models were 1.000. However, in the validation set, the auto-encoder model\'s AUC, accuracy, sensitivity, and specificity were 0.994, 0.976, 0.972, and 0.978, respectively. The remaining models\' AUC, accuracy, sensitivity, and specificity were 1.000. The DeLong test showed no statistically significant differences between the machine learning models in the training and validation sets (Z = 0, P = 1). Our study investigated the feasibility of using DCE-MRI-based radiomics features to predict HER2-low BC. Certain radiomics features showed associations with HER2-low BC and may have predictive value. Machine learning prediction models developed using these radiomics features could be beneficial for distinguishing between HER2-low and HER2-positive BC. These noninvasive preoperative models have the potential to assist in clinical decision-making for HER2-low breast cancer, thereby advancing personalized clinical precision.
摘要：
建立基于术前动态增强磁共振成像（DCE-MRI）影像组学的机器学习模型，探讨其在人类表皮生长因子受体2（HER2）与HER2阳性乳腺癌（BC）鉴别诊断中的潜在预后价值。回顾性分析2018年1月至2022年12月我院收治的233例经病理证实的浸润性乳腺癌患者。其中,103例HER2阳性，130例HER2低表达BC。采用合成少数过采样技术来解决类不平衡问题。患者以7：3的比例随机分为训练集（163例）和验证集（70例）。从DCE-MRI第二阶段成像中提取影像组学特征。Z分数归一化用于标准化影像组学特征，并利用皮尔逊相关系数和递归特征消除来探索显著特征。使用6种机器学习算法构建预测模型：逻辑回归，随机森林,支持向量机，AdaBoost,决策树,和自动编码器。构建了接收器工作特性曲线，并根据曲线下面积（AUC）评估预测模型，准确度,灵敏度,和特异性。在训练集中，AUC,准确度,灵敏度,所有模型的特异性均为1.000。然而,在验证集中，自动编码器模型的AUC，准确度,灵敏度,特异性分别为0.994、0.976、0.972和0.978。其余型号\'AUC,准确度,灵敏度,特异性为1.000。DeLong测试显示，训练集和验证集中的机器学习模型之间没有统计学上的显着差异（Z=0，P=1）。我们的研究调查了使用基于DCE-MRI的影像组学特征来预测HER2低BC的可行性。某些影像组学特征显示与HER2低BC相关，可能具有预测价值。使用这些影像组学功能开发的机器学习预测模型可能有利于区分低HER2和HER2阳性BC。这些非侵入性术前模型有可能帮助HER2低乳腺癌的临床决策。从而推进个性化临床精准化。
