Purpose

Telomerase reverse transcriptase (TERT) promoter mutation status is an important biomarker for the precision diagnosis and prognosis prediction of lower grade glioma (LGG). This study aimed to construct a radiomic signature to noninvasively predict the TERT promoter status in LGGs.

Methods

Eighty-three local patients with pathology-confirmed LGG were retrospectively included as a training cohort, and 33 patients from The Cancer Imaging Archive (TCIA) were used as for independent validation. Three types of regions of interest (ROIs), which covered the tumor, peri-tumoral area, and tumor plus peri-tumoral area, were delineated on three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted and T2-weighted images. One hundred seven shape, first-order, and texture radiomic features from each modality under each ROI were extracted and selected through least absolute shrinkage and selection operator. Radiomic signatures were constructed with multiple classifiers and evaluated using receiver operating characteristic (ROC) analysis. The tumors were also stratified according to IDH status.

Results

Three radiomic signatures, namely, tumoral radiomic signature, tumoral plus peri-tumoral radiomic signature, and fusion radiomic signature, were built, all of which exhibited good accuracy and balanced sensitivity and specificity. The tumoral signature displayed the best performance, with area under the ROC curves (AUC) of 0.948 (0.903–0.993) in the training cohort and 0.827 (0.667–0.988) in the validation cohort. In the IDH subgroups, the AUCs of the tumoral signature ranged from 0.750 to 0.940.

Conclusion

The MRI-based radiomic signature is reliable for noninvasive evaluation of TERT promoter mutations in LGG regardless of the IDH status. The inclusion of peri-tumoral area did not significantly improve the performance.

中文翻译：

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基于常规磁共振成像的放射学特征可预测II级和III级神经胶质瘤的端粒酶逆转录酶启动子突变状态。

目的

端粒酶逆转录酶（TERT）启动子的突变状态是低级神经胶质瘤（LGG）的精确诊断和预后预测的重要生物标志物。这项研究旨在构建放射性标记，以无创地预测LGG中TERT启动子的状态。

方法

回顾性纳入83例经病理学证实的LGG的本地患者作为训练队列，并将33例来自The Cancer Imaging Archive（TCIA）的患者用于独立验证。在三维对比增强的T1（3D-CE-T1）加权和T2加权上描绘了三种类型的感兴趣区域（ROI），它们覆盖了肿瘤，肿瘤周围区域以及肿瘤加上肿瘤周围区域。加权图像。通过最小绝对收缩和选择算子，从每个模态下的每个模态中提取并选择了一百七个形状，一阶和纹理放射特征。使用多个分类器构建放射标记，并使用接收器工作特征（ROC）分析对其进行评估。肿瘤也根据IDH状态分层。

结果

建立了三个放射性标记，即肿瘤放射性标记，肿瘤加肿瘤周围放射性标记和融合放射性标记，它们均显示出良好的准确性以及平衡的敏感性和特异性。肿瘤特征显示出最佳表现，在训练队列中ROC曲线下面积（AUC）为0.948（0.903-0.993），在验证队列中ROC曲线下面积为0.827（0.667-0.988）。在IDH亚组中，肿瘤特征的AUC范围为0.750至0.940。

结论

基于MRI的放射学特征可可靠地用于LGG中TERT启动子突变的无创评估，而与IDH状态无关。包括肿瘤周围区域并没有显着改善性能。