无线电技术
医学
接收机工作特性
生物标志物
磁共振成像
胶质瘤
甲基化
人工智能
肿瘤科
放射科
内科学
癌症研究
计算机科学
生物化学
基因
化学
作者
Chendan Jiang,Ziren Kong,Sirui Liu,Suying Feng,Yiwei Zhang,Ruizhe Zhu,Wenlin Chen,Yuekun Wang,Yuelei Lyu,Hui You,Dachun Zhao,Renzhi Wang,Wenbin Ma,Feng Feng
标识
DOI:10.1016/j.ejrad.2019.108714
摘要
Purpose The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has been proven to be a prognostic and predictive biomarker for lower grade glioma (LGG). This study aims to build a radiomics model to preoperatively predict the MGMT promoter methylation status in LGG. Method 122 pathology-confirmed LGG patients were retrospectively reviewed, with 87 local patients as the training dataset, and 35 from The Cancer Imaging Archive as independent validation. A total of 1702 radiomics features were extracted from three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted and T2-weighted MRI images, including 14 shape, 18 first order, 75 texture, and 744 wavelet features respectively. The radiomics features were selected with the least absolute shrinkage and selection operator algorithm, and prediction models were constructed with multiple classifiers. Models were evaluated using receiver operating characteristic (ROC). Results Five radiomics prediction models, namely, 3D-CE-T1-weighted single radiomics model, T2-weighted single radiomics model, fusion radiomics model, linear combination radiomics model, and clinical integrated model, were built. The fusion radiomics model, which constructed from the concatenation of both series, displayed the best performance, with an accuracy of 0.849 and an area under the curve (AUC) of 0.970 (0.939-1.000) in the training dataset, and an accuracy of 0.886 and an AUC of 0.898 (0.786-1.000) in the validation dataset. Linear combination of single radiomics models and integration of clinical factors did not improve. Conclusions Conventional MRI radiomics models are reliable for predicting the MGMT promoter methylation status in LGG patients. The fusion of radiomics features from different series may increase the prediction performance.
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