Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

被引:18
作者
Kong, Ziren [1 ,2 ]
Jiang, Chendan [1 ,2 ]
Zhang, Yiwei [3 ]
Liu, Sirui [2 ,3 ]
Liu, Delin [1 ,2 ]
Liu, Zeyu [2 ,3 ]
Chen, Wenlin [1 ,2 ]
Liu, Penghao [1 ,2 ]
Yang, Tianrui [1 ,2 ]
Lyu, Yuelei [2 ,3 ,4 ]
Zhao, Dachun [2 ,5 ]
You, Hui [2 ,3 ]
Wang, Yu [1 ,2 ]
Ma, Wenbin [1 ,2 ]
Feng, Feng [2 ,3 ]
机构
[1] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Neurosurg, Beijing, Peoples R China
[2] Peking Union Med Coll, Beijing, Peoples R China
[3] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Radiol, Beijing, Peoples R China
[4] Capital Med Univ, Beijing Chao Yang Hosp, Dept Radiol, Beijing, Peoples R China
[5] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Pathol, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
MRI; radiomic; glioma; chromosomal; 1p; 19q co-deletion; spacing; CENTRAL-NERVOUS-SYSTEM; TEXTURE ANALYSIS; IDH; CLASSIFICATION; CODELETION; DIFFUSION; PERFUSION; RESECTION; SURVIVAL; MUTATION;
D O I
10.3389/fneur.2020.551771
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG. Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed. Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950-1.000. Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures.
引用
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页数:10
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