Liu Yan, Zheng Zhiming, Wang Zhiyuan, Qian Xusheng, Yao Zhigang, Cheng Chenchen, Zhou Zhiyong, Gao Fei, Dai Yakang
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Quant Imaging Med Surg. 2023 Apr 1;13(4):2143-2155. doi: 10.21037/qims-22-836. Epub 2023 Mar 2.
Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the treatment strategy selection and prognosis evaluation of glioma. The purpose of this study is to predict the IDH mutation status of gliomas based on multicenter magnetic resonance (MR) images using radiomic models, which were composed from the selected radiomics features and logistic regression (LR), support vector machine (SVM), and LR least absolute shrinkage and selection operator (LASSO) classifiers.
We retrospectively reviewed the medical records of 205 patients with gliomas. We enrolled 78 patients from Shandong Provincial Hospital from January 2018 to December 2019 as testing sets and 127 patients from The Cancer Genome Atlas (TCGA) as training sets. Preoperative MR images were stratified according to their IDH status, and the participants formed a consecutive and random series. Four MR modalities, including T1C, T2, T1 fluid-attenuated inversion recovery (FLAIR), and T2 FLAIR, were used for analysis. Five-fold cross-validation was adopted to train the models, and the models' performances were verified through the testing set. Tumor volumes of interest (VOI) were delineated on the 4 MR modalities. A total of 428 radiomics features were extracted. Two feature selection algorithms, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), were used to select radiomics features. These features were fed into 3 machine learning classifiers, which were LR, SVM, and LR LASSO, to construct prediction models. The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and area under the curve (AUC) were applied to measure the predictive performance of the radiomics models.
The LR (SVM and LR LASSO) classifier predicted IDH mutation status with an average testing set ACC of 80.77% (80.64% and 80.41%), a SEN of 73.68% (84.21% and 89.47%), a SPEC of 87.50% (67.50% and 62.50%), and an AUC of 0.8572 (0.8217 and 0.8164).
The radiomics models based on MR modalities demonstrated the potential to be used as tools across different data sets for the noninvasive prediction of the IDH mutation status in glioma.
异柠檬酸脱氢酶（IDH）突变状态是胶质瘤治疗策略选择和预后评估的重要生物标志物。本研究的目的是使用由选定的放射组学特征和逻辑回归（LR）、支持向量机（SVM）以及LR最小绝对收缩和选择算子（LASSO）分类器组成的放射组学模型，基于多中心磁共振（MR）图像预测胶质瘤的IDH突变状态。
我们回顾性分析了205例胶质瘤患者的病历。我们纳入了2018年1月至2019年12月来自山东省立医院的78例患者作为测试集，以及来自癌症基因组图谱（TCGA）的127例患者作为训练集。术前MR图像根据其IDH状态进行分层，参与者构成连续随机序列。使用包括T1加权增强（T1C）、T2加权像（T2）、T1液体衰减反转恢复序列（FLAIR）和T2 FLAIR在内的四种MR成像方式进行分析。采用五折交叉验证来训练模型，并通过测试集验证模型性能。在四种MR成像方式上勾勒出感兴趣的肿瘤体积（VOI）。共提取了428个放射组学特征。使用两种特征选择算法，即皮尔逊相关系数（PCC）和递归特征消除（RFE）来选择放射组学特征。将这些特征输入到三种机器学习分类器，即LR、SVM和LR LASSO中，以构建预测模型。应用准确率（ACC）、灵敏度（SEN）、特异性（SPEC）和曲线下面积（AUC）来衡量放射组学模型的预测性能。
LR（SVM和LR LASSO）分类器预测IDH突变状态时，测试集的平均ACC为80.77%（80.64%和80.41%），SEN为73.68%（84.21%和89.47%），SPEC为87.50%（67.50%和62.50%），AUC为0.8572（0.8217和0.8164）。
基于MR成像方式的放射组学模型显示出有潜力作为跨不同数据集的工具，用于胶质瘤IDH突变状态的无创预测。
