Conventional magnetic resonance imaging–based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas.
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:...
| Publicado en: | Neuroradiology Vol. 62; no. 7; pp. 803 - 814 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144203159&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144203159 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jul2020 vid: 62 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144203159 144183168 144203159 144203159 10.1007/s00234-020-02392-1 144203159 ppf: 803 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Conventional magnetic resonance imaging–based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas. aug: au: Jiang, Chendan Kong, Ziren Zhang, Yiwei Liu, Sirui Liu, Zeyu Chen, Wenlin Liu, Penghao Liu, Delin Wang, Yaning Lyu, Yuelei Zhao, Dachun Wang, Yu You, Hui Feng, Feng Ma, Wenbin affil: Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China sug: subj: Magnetic Resonance Imaging Methods Algorithms Telomerase Promoters Mutation Neoplasm Grading Glioma Familial and Genetic Human Male Cancer Patients Retrospective Design ROC Curve Descriptive Statistics Sensitivity and Specificity Comparative Studies Male ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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