AUTHOR=Kong Ziren , Jiang Chendan , Zhang Yiwei , Liu Sirui , Liu Delin , Liu Zeyu , Chen Wenlin , Liu Penghao , Yang Tianrui , Lyu Yuelei , Zhao Dachun , You Hui , Wang Yu , Ma Wenbin , Feng Feng 

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

JOURNAL=Frontiers in Neurology

VOLUME=11

YEAR=2020

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.551771

DOI=10.3389/fneur.2020.551771

ISSN=1664-2295

ABSTRACT=<p><bold>Objective:</bold> 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.</p><p><bold>Methods:</bold> Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (<italic>n</italic> = 78) and validation (<italic>n</italic> = 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.</p><p><bold>Results:</bold> 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.</p><p><bold>Conclusions:</bold> 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.</p>