Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Department of Ultrasound, Affiliated Union Hospital of Fujian Medical University, Fuzhou, Fu Jian 350000, China.
Clin Radiol. 2020 Jul;75(7):561.e1-561.e11. doi: 10.1016/j.crad.2020.02.011. Epub 2020 Mar 14.
To investigate the effect of radiomics in the assessment of alterations in canonical cancer pathways in breast cancer.
Eighty-eight biopsy-proven breast cancer cases were included in the present study. Radiomics features were extracted from T1-weighted sagittal dynamic contrast-enhanced magnetic resonance imaging (MRI) images. Radiomics signatures were developed to predict genetic alterations in the cell cycle, Myc, PI3K, RTK/RAS, and p53 signalling pathways by using hypothesis testing combined with least absolute shrinkage and selection operator (LASSO) regression analysis. The predictive powers of the models were examined by the area under the curve (AUC) of the receiver operating characteristic curve.
A total of 5,234 radiomics features were obtained from MRI images based on the tumour region of interest. Hypothesis tests screened 250, 229, 156, 785, and 319 radiomics features that were differentially displayed between cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations and no alteration status. According to the LASSO algorithm, 11, 12, 12, 15, and 13 features were identified for the construction of the radiomics signatures to predict cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations, with AUC values of 0.933, 0.926, 0.956, 0.940, and 0.886, respectively. The cell cycle radiomics score correlated closely with the RTK/RAS and p53 radiomics scores. These signatures were also dysregulated in patients with different oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 statuses.
MRI-based radiogenomics analysis exhibits excellent performance in predicting genetic pathways alterations, thus providing a novel approach for non-invasively obtaining genetic-level molecular characteristics of tumours.
探讨影像组学在评估乳腺癌中经典癌症通路改变中的作用。
本研究纳入 88 例经活检证实的乳腺癌病例。从 T1 加权矢状位动态对比增强磁共振成像（MRI）图像中提取影像组学特征。通过假设检验结合最小绝对值收缩和选择算子（LASSO）回归分析，开发影像组学特征来预测细胞周期、Myc、PI3K、RTK/RAS 和 p53 信号通路的基因改变。通过受试者工作特征曲线下面积（AUC）评估模型的预测能力。
基于肿瘤感兴趣区，从 MRI 图像中获得了总共 5234 个影像组学特征。假设检验筛选出了 250、229、156、785 和 319 个在细胞周期、Myc、PI3K、RTK/RAS 和 p53 改变和无改变状态之间差异表达的影像组学特征。根据 LASSO 算法，确定了 11、12、12、15 和 13 个特征来构建预测细胞周期、Myc、PI3K、RTK/RAS 和 p53 改变的影像组学特征，其 AUC 值分别为 0.933、0.926、0.956、0.940 和 0.886。细胞周期影像组学评分与 RTK/RAS 和 p53 影像组学评分密切相关。这些特征在不同雌激素受体、孕激素受体和人表皮生长因子受体 2 状态的患者中也存在失调。
基于 MRI 的放射组学分析在预测基因通路改变方面表现出优异的性能，从而为非侵入性地获得肿瘤的基因水平分子特征提供了一种新方法。
