基于T2WI影像组学标签预测乳腺癌人表皮生长因子受体2表达状态

目的 探讨基于MR T2WI的影像组学标签在术前预测乳腺癌人表皮生长因子受体2（HER2）表达状态的价值。方法 回顾性收集209例乳腺癌患者的T2WI，将患者随机分为训练组（n=145）和验证组（n=64）。手动勾画病灶ROI，并于Matlab 2013a平台中提取组学特征。通过组间相关系数及最小绝对收缩和选择算子逻辑回归模型筛选组学特征并构建组学标签。比较HER2表达阳性与阴性亚组患者的影像组学得分差异，采用ROC曲线评价训练组中影像组学标签预测HER2的效能，并以获得的预测阈值用于验证组中进行验证。结果 最终获得由13个组学特征构成的影像组学标签。在训练组及验证组中，HER2阳性亚组与阴性亚组患者间组学得分差异均有统计学意义（P均<0.05）。基于T2WI的影像组学标签在训练组及验证组中的AUC分别为0.798、0.707。结论 基于T2WI构建的影像组学标签对术前预测乳腺癌HER2表达状态具有一定价值。

T2WI-based radiomics signatures for predicting human epidermal growth factor receptor 2 status in breast cancer

Objective To investigate the value of T2WI-based radiomics signatures for preoperatively prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer. Methods T2WI of 209 patients with breast cancer were retrospectively analyzed. The patients were randomly divided into training group (n=145) and validation group (n=64). The ROIs were manually delineated around the tumor profile. Radiomics feature extraction was implemented in MATLAB 2013a. The interclass correlation coefficients, the least absolute shrinkage and selection operator Logistic regression analysis were used for radiomics features selection and generation. The difference of the Rad-score between HER2-positive and HER2-negative subgroups was observed. The predictive performances of the radiomics signatures for HER2 status were evaluated with ROC curves in training group, and were validated in validation group with the obtained predictive threshold. Results The radiomics signatures were constituted by 13 selective features. In both training and validation groups, there were statistically significant differences in Rad-score between HER2-positive subgroup and HER2-negative subgroup (both P<0.05). T2WI-based radiomics signatures exhibited good discrimination for HER2 status, with AUC of 0.798 in training group and 0.707 in validation group. Conclusion The radiomics signatures based on T2WI have certain value for preoperative prediction of HER2 status in breast cancer.