Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas
Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas
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DOI：
10.1007/s00234-020-02392-1
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发表时间：
2020-04-01
期刊：
影响因子：
2.8
通讯作者：
Ma, Wenbin
中科院分区：
文献类型：
--
作者：
Jiang, Chendan;Kong, Ziren;Ma, Wenbin
目的端粒酶逆转录酶（TERT）基因启动子突变状态是低级别胶质瘤（LGG）精确诊断和预后预测的重要生物学指标。本研究的目的是构建一个放射组学标记，以非侵入性地预测在LGGs中的TERT启动子状态。方法回顾性纳入83例经病理证实的LGG患者作为训练队列，并以33例来自肿瘤影像资料库（TCIA）的患者作为独立验证。在三维增强T1（3D-CE-T1）加权和T2加权图像上描绘三种类型的感兴趣区（ROI），包括肿瘤、肿瘤周围区域和肿瘤+肿瘤周围区域。从每个ROI下的每个模态中提取107个形状、一阶和纹理放射组学特征，并通过最小绝对收缩和选择算子进行选择。放射组学特征用多个分类器构建，并使用受试者工作特征（ROC）分析进行评价。还根据IDH状态对肿瘤进行分层。结果建立了肿瘤放射组学标记、肿瘤加瘤周放射组学标记和融合放射组学标记3种放射组学标记，均具有良好的准确性和平衡的敏感性和特异性。肿瘤特征显示出最佳性能，训练队列中的ROC曲线下面积（AUC）为0.948（0.903-0.993），验证队列中为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。结论无论IDH与否，基于MRI的放射组学标记技术均能无创性地检测LGG中TERT基因启动子突变。纳入肿瘤周围区域并未显著改善性能。
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.