Clinical application value of multi ⁃ parameter MRI radiomics evaluation of HER ⁃ 2 expression status in invasive breast cancer
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摘要:
目的：探讨基于多参数磁共振成像（magnetic resonance imaging，MRI）影像组学特征的模型预测浸润性乳腺癌人类表皮生长因子受体-2（human epidermal growth factor receptor-2，HER-2）表达状态的价值。方法：回顾性分析南京医科大学第一附属医院2018年1月—2019年12月401例乳腺癌患者的基线期MRI图像及临床资料。使用ITK-SNAP软件在快速反转恢复序列（turbo inversion recovery magnitude，TIRM）、动态对比增强磁共振成像第2期（dynamic-contrast enhanced magnetic resonance imaging phase 2，DCE2）、动态对比增强磁共振成像第 4 期（dynamic-contrast enhanced magnetic resonance imaging phase 4， DCE4）、弥散加权成像（diffusion-weighted imaging，DWI）和表观弥散系数（apparent diffusion coefficient，ADC）的最大肿瘤层面手动勾画二维感兴趣区域（region of interest，ROI），并对所勾画的ROI区域进行特征提取及降维筛选。应用逻辑回归（logistic re- gression，LR）算法建立预测HER-2表达状态的单参数模型、组合模型和多参数模型。结果：最终筛选出26个最优特征，其中按权重排序位居首位的特征为DCE2_original_shape_SurfaceVolumeRatio。单参数模型中预测效能最好的是DCE2模型，训练集及测试集的曲线下面积（area under curve，AUC）分别为0.907、0.879；组合模型中联合增强特征的模型比其他未联合增强特征的模型预测效能更好（P均≤ 0.001）；多参数模型预测效能最佳（训练集及测试集的AUC值分别为0.932、0.906）。结论：基于多参数影像特征构建的影像组学模型评估浸润性乳腺癌HER-2表达状态有一定的临床价值，其中增强早期特征的预测价值较高。
Abstract:
Objective：To investigate the value of a model based on multi -parameter magnetic resonance imaging（MRI）radiomics features in predicting the expression status of human epidermal growth factor receptor-2（HER -2）in invasive breast cancer patients. Methods：A retrospective analysis was conducted on baseline MRI images and clinical data of 401 breast cancer patients from January 2018 to December 2019 at the First Affiliated Hospital of Nanjing Medical University. The 2D region of interest（ROI）were segmented manually on ITK-SNAP software at the maximum tumor level of turbo inversion recovery magnitude（TIRM），dynamic-contrast enhanced magnetic resonance imaging phase 2（DCE2），dynamic -contrast enhanced magnetic resonance imaging phase 4（DCE4）， diffusion-weighted imaging（DWI）and apparent diffusion coefficient（ADC）. Feature extraction and dimensionality reduction screening were performed on the delineated ROIs. Logistic regression（LR）algorithm was used to construct single-parameter，combination and multi-parameter models for predicting HER-2 expression status. Results：A total of 26 optimal radiomics features were selected，with DCE2_original_shape_SurfaceVolumeRatio ranking first in terms of weight. Among the single-parameter models，the DCE2 model showed the best prognostic efficacy（AUC values of 0.907 and 0.879 for the training and test sets，respectively）. Among the combined models，the combined with DCE model had better predictive performance than the models without DCE（all P values ≤0.001）. The multi-parameter model had the best predictive performance（AUC values of 0.932 and 0.906 for the training and test sets， respectively）. Conclusion：The radiomics model based on multi-parameter radiomics features has certain clinical value in evaluating the HER-2 expression status in invasive breast cancer，with higher predictive value for early enhancement features.
