连接组学
逻辑回归
判别式
医学
Lasso(编程语言)
连接体
磁共振弥散成像
多项式logistic回归
人工智能
磁共振成像
计算机科学
机器学习
放射科
内科学
生物
神经科学
功能连接
万维网
作者
Ne Yang,Xiong Xiao,Guocan Gu,Xianyu Wang,Xinran Zhang,Yi Wang,Changcun Pan,Peng Zhang,Longfei Ma,Liwei Zhang,Hongen Liao
标识
DOI:10.1016/j.radonc.2023.109789
摘要
Purpose To establish an individualized predictive model to identify patients with brainstem gliomas (BSGs) at high risk of H3K27M mutation, with the inclusion of brain structural connectivity analysis based on diffusion MRI (dMRI). Materials and Methods A primary cohort of 133 patients with BSGs (80 H3K27M-mutant) were retrospectively included. All patients underwent preoperative conventional MRI and dMRI. Tumor radiomics features were extracted from conventional MRI, while two kinds of global connectomics features were extracted from dMRI. A machine learning-based individualized H3K27M mutation prediction model combining radiomics and connectomics features was generated with a nested cross validation strategy. Relief algorithm and SVM method were used in each outer LOOCV loop to select the most robust and discriminative features. Additionally, two predictive signatures were established using the LASSO method, and simplified logistic models were built using multivariable logistic regression analysis. An independent cohort of 27 patients was used to validate the best model. Results 35 tumor-related radiomics features, 51 topological properties of brain structural connectivity networks, and 11 microstructural measures along white matter tracts were selected to construct a machine learning-based H3K27M mutation prediction model, which achieved an AUC of 0.9136 in the independent validation set. Radiomics- and connectomics-based signatures were generated and simplified combined logistic model was built, upon which derived nomograph achieved an AUC of 0.8827 in the validation cohort. Conclusion dMRI is valuable in predicting H3K27M mutation in BSGs, and connectomics analysis is a promising approach. Combining multiple MRI sequences and clinical features, the established models have good performance.
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