An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer
An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer
复制标题
基于 MRI 的放射组学分类器用于术前预测乳腺癌 Ki-67 状态
DOI：
10.1016/j.acra.2018.01.006
复制
发表时间：
2018-09-01
影响因子：
4.8
通讯作者：
Liu, Zaiyi
中科院分区：
文献类型：
--
作者：
Liang, Cuishan;Cheng, Zixuan;Liu, Zaiyi
理论基础和目的：本研究旨在探讨基于磁共振成像的放射组学分类器在乳腺癌患者术前预测Ki-67状态的价值。材料和方法：我们将318例经临床病理证实的乳腺癌患者按时间顺序分为训练数据集(n=200)和验证数据集(n=118)。从乳腺癌的T2加权(T2W)和增强T1加权(T1+C)图像中提取放射组学特征。使用最小绝对收缩和选择算子回归分析方法生成放射组学特征选择和放射组学分类器。探讨乳腺癌患者的放射组学分类与Ki-67状态的相关性。结果：通过放射组学特征选择，分别选取了16个和14个基于T2W和T1+C图像的特征组成放射组学分类器。在训练和验证数据集中，基于T2W图像的放射组学分类器与Ki-67状态显著相关(P<；.0001)。基于T1+C图像的放射组学分类器与训练数据集中的Ki-67状态显著相关(P<；0.0001)，但在验证数据集中没有相关性(P=.083)。基于T2W图像的放射组学分类器在训练和验证数据集中对Ki-67状态的识别率分别为0.762(95%可信区间：0.685，0.838)和0.740(95%可信区间：0.645，0.836)。结论：基于T2W图像的放射组学分类器是乳腺癌患者Ki-67状态的重要预测指标。因此，在临床实践中，它可以作为一种非侵入性方法促进术前对Ki-67状态的预测。
Rationale and Objectives: This study aims to investigate the value of a magnetic resonance imaging-based radiomics classifier for preoperatively predicting the Ki-67 status in patients with breast cancer.Materials and Methods: We chronologically divided 318 patients with clinicopathologically confirmed breast cancer into a training dataset (n = 200) and a validation dataset (n = 118). Radiomics features were extracted from T2-weighted (T2W) and contrast-enhanced T1-weighted (T1+C) images of breast cancer. Radiomics feature selection and radiomics classifiers were generated using the least absolute shrinkage and selection operator regression analysis method. The correlation between the radiomics classifiers and the Ki-67 status in patients with breast cancer was explored. The predictive performances of the radiomics classifiers for the Ki-67 status were evaluated with receiver operating characteristic curves in the training dataset and validated in the validation dataset.Results: Through the radiomics feature selection, 16 and 14 features based on T2W and T1+C images, respectively, were selected to constitute the radiomics classifiers. The radiomics classifier based on T2W images was significantly correlated with the Ki-67 status in both the training and the validation datasets (both P < .0001). The radiomics classifier based on T1+C images was significantly correlated with the Ki-67 status in the training dataset (P < .0001) but not in the validation dataset (P = .083). The T2W image-based radiomics classifier exhibited good discrimination for Ki-67 status, with areas under the receiver operating characteristic curves of 0.762 (95% confidence interval: 0.685, 0.838) and 0.740 (95% confidence interval: 0.645, 0.836) in the training and validation datasets, respectively.Conclusions: The T2W image-based radiomics classifier was a significant predictor of Ki-67 status in patients with breast cancer. Thus, it may serve as a noninvasive approach to facilitate the preoperative prediction of Ki-67 status in clinical practice.