基于影像组学的脑胶质瘤分级方法

A glioma grading method based on radiomics

目的探讨依照影像组学的理论和方法对脑胶质瘤进行分级的可行性。方法2012至2016年回顾性纳入161例经病理证实的脑胶质瘤患者，其中低级别胶质瘤52例，高级别胶质瘤109例。对所有患者的MRI图像进行高通量的数据采集，提取形状、密度、纹理、小波等346个量化特征，利用互信息和logistic回归模型，进行特征降维和预测模型选择，最后在数据集上使用十折交叉验证对模型的预测能力进行验证。结果本研究预测模型最终获得19个特征。模型的敏感度为96．3％（105／109），特异度为78．8％（41／52），曲线下面积（AUC）为0．9527，模型准确率为90．7％（146／161）。结论本研究提出的影像组学方法具有无创、计算速度快、正确率高等优点，可以为脑胶质瘤的临床分级提供辅助手段。

Objective To explore the classification of gliomas according to the theory and method of radiomics. Methods [n this study, 16l pathologically confirmed glioma patients were retrospectively selected from 2012 to 2016 including 52 low-grade gliomas and 109 high-grade gliomas. Three hundred and forty-six quantization features were extracted from the MRI images, including shape, density, texture and wavelet imaging features. Mutual information and logistic regression model were used to select feature reduction and prediction model. The predictive ability of the model was validated using 10-fold cross-validation. Results Nineteen radiomics features were chosen from 346 quantization features. The sensitivity of the model was 96.3% （105/109）, the specificity was 78.8% （41/52）, the area under the curve （AUC） was 0.952 7, and the accuracy was 90.7% （146/161）. Conclusion The solution proposed in this paper showed that radiomics can non-invasively and quickly provide an adjunct to the clinical grade of glioma with high accuracy.