He Ailing, Wang Peng, Zhu Aihua, Liu Yankui, Chen Jianhuan, Liu Li
Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi 214122, China.
Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, China.
Diagnostics (Basel). 2022 Nov 30;12(12):2995. doi: 10.3390/diagnostics12122995.
The IDH somatic mutation status is an important basis for the diagnosis and classification of gliomas. We proposed a "6-Step" general radiomics model to noninvasively predict the IDH mutation status by simultaneously tuning combined multi-sequence MRI and optimizing the full radiomics processing pipeline. Radiomic features ( = 3776) were extracted from multi-sequence MRI (T1, T2, FLAIR, and T1Gd) in low-grade gliomas (LGGs), and a total of 45,360 radiomics pipeline were investigated according to different settings. The predictive ability of the general radiomics model was evaluated with regards to accuracy, stability, and efficiency. Based on numerous experiments, we finally reached an optimal pipeline for classifying IDH mutation status, namely the T2+FLAIR combined multi-sequence with the wavelet image filter, mean data normalization, PCC dimension reduction, RFE feature selection, and SVM classifier. The mean and standard deviation of AUC, accuracy, sensitivity, and specificity were 0.873 ± 0.05, 0.876 ± 0.09, 0.875 ± 0.11, and 0.877 ± 0.15, respectively. Furthermore, 14 radiomic features that best distinguished the IDH mutation status of the T2+FLAIR multi-sequence were analyzed, and the gray level co-occurrence matrix (GLCM) features were shown to be of high importance. Apart from the promising prediction of the molecular subtypes, this study also provided a general tool for radiomics investigation.
异柠檬酸脱氢酶（IDH）体细胞突变状态是胶质瘤诊断和分类的重要依据。我们提出了一种“六步法”通用放射组学模型，通过同时调整多序列MRI组合并优化整个放射组学处理流程，来无创预测IDH突变状态。从低级别胶质瘤（LGG）的多序列MRI（T1、T2、液体衰减反转恢复序列（FLAIR）和T1加权增强扫描（T1Gd））中提取了放射组学特征（n = 3776），并根据不同设置对总共45360种放射组学流程进行了研究。从准确性、稳定性和效率方面评估了通用放射组学模型的预测能力。基于大量实验，我们最终得出了一种用于分类IDH突变状态的最佳流程，即采用小波图像滤波器、均值数据归一化、皮尔逊相关系数（PCC）降维、递归特征消除（RFE）特征选择和支持向量机（SVM）分类器的T2 + FLAIR多序列组合。曲线下面积（AUC）、准确性、敏感性和特异性的均值及标准差分别为0.873±0.05、0.876±0.09、0.875±0.11和0.877±0.15。此外，分析了最能区分T2 + FLAIR多序列IDH突变状态的14个放射组学特征，结果显示灰度共生矩阵（GLCM）特征具有高度重要性。除了对分子亚型有前景的预测外，本研究还为放射组学研究提供了一种通用工具。
