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Conventional magnetic resonance imaging–based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas

  • Diagnostic Neuroradiology
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Abstract

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.

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Funding

This study was funded by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (Grant number 2016-I2M-2-001), the Fundamental Research Funds for the Central Universities (Grant number 3332018029), and the National Natural Science Foundation of China (Grant number 81601485).

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Author notes

  1. CJ and ZK contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Chendan Jiang, Ziren Kong, Wenlin Chen, Penghao Liu, Delin Liu, Yaning Wang, Yu Wang & Wenbin Ma

  2. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, China

    Yiwei Zhang, Sirui Liu, Zeyu Liu, Yuelei Lyu, Hui You & Feng Feng

  3. Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China

    Yuelei Lyu

  4. Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Dachun Zhao

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  1. Chendan Jiang
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Correspondence to Hui You.

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Jiang, C., Kong, Z., Zhang, Y. et al. Conventional magnetic resonance imaging–based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas. Neuroradiology 62, 803–813 (2020). https://doi.org/10.1007/s00234-020-02392-1

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