Zhao Suhong, Chen Peipei, Wang Xiaojuan, Zheng Zhaoxiu, Hui Ruirui, Pang Guodong
Department of Radiology, The Second Hospital of Shandong University, Jinan, China.
Department of Radiology, Shandong Linglong Yingcheng Hospital, Yantai, China.
Quant Imaging Med Surg. 2024 Dec 5;14(12):8387-8401. doi: 10.21037/qims-24-428. Epub 2024 Nov 29.
Preoperative prediction of human epidermal growth factor receptor 2 (HER2)-low expression using magnetic resonance imaging (MRI) can enhance the selection of clinical treatment strategies and enhance patient outcomes. Herein, we investigated the value of a neural network model constructed with multiparametric MRI in diagnosing HER2-low breast cancer.
This retrospective study involved two different centers. A total of 895 breast cancer patients (903 lesions) were enrolled from the Second Hospital of Shandong University (known as "Center 1") between January 2015 to December 2022. They were allocated to the training set (626 cases/632 lesions) and the internal validation set (269 cases/271 lesions). The external validation set included 100 patients (100 lesions) from the Qilu Hospital of Shandong University (referred to as "Center 2") between June 2021 to December 2022. All patients were subgrouped into HER2-low and HER2-0 expression groups. We used t-tests, Wilcoxon rank sum tests, and Chi-squared tests or Fisher's exact test to compare the dynamic contrast-enhanced MRI features (morphological/hemodynamic features), and the apparent diffusion coefficient (ADC) values. A neural network model was constructed using the Neuralnet package in R, with the architecture specified as c(5,2) for the hidden layers. Bootstrapping was used for internal validation. The diagnostic performance in the training set was analyzed using receiver operating characteristic (ROC) curves. The clinical effectiveness of the model was validated using a decision curve analysis (DCA).
HER2-low breast cancer lesions had irregular morphology, high early enhancement rate, and low ADC value compared to HER2-0 expressed lesions. The differences were significant (P<0.05). We then constructed a neural network model using these significant variables. ROC analysis showed that the area under the ROC curve of the model for diagnosing HER2-low breast cancer in the training, internal validation, and external validation sets was 0.757 [95% confidence interval (CI): 0.712-0.802], 0.728 (95% CI: 0.658-0.798), and 0.791 (95% CI: 0.693-0.890), respectively. The DCA demonstrated that the net benefit of the model was significantly greater than zero at a predicted probability of 0.764.
The neural network model based on MRI features is an effective tool in predicting HER2-low breast cancer, which may facilitate clinical treatment decision-making.
利用磁共振成像（MRI）对人表皮生长因子受体2（HER2）低表达进行术前预测，可优化临床治疗策略的选择并改善患者预后。在此，我们研究了基于多参数MRI构建的神经网络模型在诊断HER2低表达乳腺癌中的价值。
这项回顾性研究涉及两个不同中心。2015年1月至2022年12月期间，山东大学第二医院（称为“中心1”）共纳入895例乳腺癌患者（903个病灶）。他们被分配到训练集（626例/632个病灶）和内部验证集（269例/271个病灶）。外部验证集包括2021年6月至2022年12月期间山东大学齐鲁医院（称为“中心2”）的100例患者（100个病灶）。所有患者被分为HER2低表达组和HER2零表达组。我们使用t检验、Wilcoxon秩和检验、卡方检验或Fisher精确检验来比较动态对比增强MRI特征（形态学/血流动力学特征）和表观扩散系数（ADC）值。使用R语言中的Neuralnet包构建神经网络模型，隐藏层结构指定为c(5,2)。采用自抽样法进行内部验证。使用受试者操作特征（ROC）曲线分析训练集中模型的诊断性能。使用决策曲线分析（DCA）验证模型的临床有效性。
与HER2零表达的病灶相比，HER2低表达的乳腺癌病灶形态不规则、早期强化率高且ADC值低。差异具有统计学意义（P<0.05）。然后，我们使用这些显著变量构建了一个神经网络模型。ROC分析显示，该模型在训练集、内部验证集和外部验证集中诊断HER2低表达乳腺癌的ROC曲线下面积分别为0.757 [95%置信区间（CI）：0.712 - 0.802]、0.728（95% CI：0.658 - 0.798）和0.791（95% CI：0.693 - 0.890）。DCA表明，在预测概率为0.764时，该模型的净效益显著大于零。
基于MRI特征的神经网络模型是预测HER2低表达乳腺癌的有效工具，可能有助于临床治疗决策。
