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  • Title: [Preoperative prediction of HER-2 expression status in breast cancer based on MRI radiomics model].
    Author: Zhang Y, Huang H, Yin L, Wang ZX, Lu SY, Wang XX, Xiang LL, Zhang Q, Zhang JL, Shan XH.
    Journal: Zhonghua Zhong Liu Za Zhi; 2024 May 23; 46(5):428-437. PubMed ID: 38742356.
    Abstract:
    Objective: This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer. Methods: A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness. Results: The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model (P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model (P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort (P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort (P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions: T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer. 目的： 探讨多模态MRI影像组学模型术前预测乳腺癌人表皮生长因子受体2（HER-2）表达状态的价值。 方法： 纳入2021年1月至2023年5月镇江市第一人民医院经术后病理诊断的女性乳腺癌患者187例，其中HER-2阴性139例，HER-2阳性48例。将患者分为训练集（131例）和验证集（56例），利用训练集构建预测模型，利用验证组对预测模型进行验证。在T2加权成像（T2WI）、表观扩散系数（ADC）及动态增强扫描第2期（DCE-2）、第6期（DCE-6）MRI图像中逐层勾画乳腺癌原发灶三维立体感兴趣区，使用Pyradiomic软件提取960个影像组学特征，通过重复性分析、Pearson相关分析及最小绝对收缩和选择算子回归方法进行筛选和降维后，建立影像组学标签，采用logistic回归分析分别构建预测乳腺癌HER-2表达状态的T2WI影像组学模型、ADC影像组学模型、DCE-2影像组学模型、DCE-6影像组学模型和联合序列影像组学模型。基于患者的临床、病理及MRI影像特征，采用单因素和多因素logistic回归分析构建临床病理MRI特征模型。利用患者的影像组学标签联合筛选得到的有统计学意义的临床病理MRI特征，构建列线图模型。采用受试者工作特性（ROC）曲线评价各模型的预测效能，绘制决策曲线评价列线图模型的临床获益。 结果： 成功构建了预测乳腺癌HER-2表达状态的T2WI影像组学模型、ADC影像组学模型、DCE-2影像组学模型、DCE-6影像组学模型、联合序列影像组学模型、临床病理MRI特征模型和列线图模型。ROC曲线分析显示，在训练集和验证集中，T2WI影像组学模型的曲线下面积（AUC）分别为0.797和0.760，ADC影像组学模型的AUC分别为0.776和0.634，DCE-2影像组学模型的AUC分别为0.804和0.759，DCE-6影像组学模型的AUC分别为0.869和0.798，联合序列影像组学模型的AUC分别为0.908和0.847，临床病理MRI特征模型的AUC分别为0.703和0.693，列线图模型的AUC分别为0.938和0.859。在训练集中，联合序列影像组学模型的AUC高于T2WI、ADC、DCE-2影像组学模型和临床病理MRI特征模型（均P＜0.05）；在验证集中，联合序列影像组学模型的AUC高于ADC影像组学模型（P＜0.01），但与临床病理MRI特征模型差异无统计学意义（P＞0.05）。在训练集中列线图模型的AUC高于全部单模态影像组学模型和临床病理MRI特征模型（均P＜0.05），在验证集中列线图模型的AUC高于ADC影像组学模型、DCE-2影像组学模型和临床病理MRI特征模型（均P＜0.05）。在训练集和验证集中列线图模型的AUC与联合序列影像组学模型差异均无统计学意义（均P＞0.05）。决策曲线显示，列线图模型术前预测乳腺癌HER-2表达状态的临床净获益明显优于临床病理预测模型。 结论： 基于T2WI、ADC和早期-延迟期DCE MRI的多模态MRI影像组学模型能高效预测术前乳腺癌HER-2表达状态，有望用于乳腺癌HER-2状态的术前无创评估，为乳腺癌术前新辅助治疗方案的决策提供依据。.
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