Rationale and Objectives
The World Health Organization 2016 classification of central nervous system tumors
added the molecular classification of gliomas and has guiding significance for the
operation and prognosis of glioma patients. At present, the perfusion technique plays
an important role in judging the malignant degree of glioma. To evaluate the performance
of dynamic susceptibility contrast (DSC)- and dynamic contrast-enhanced (DCE)-magnetic
resonance imaging (MRI) histogram analyses in discriminating the states of molecular
biomarkers and survival in glioma patients.
Materials and Methods
Forty-three glioma patients who underwent DCE- and DSC-MRI were enrolled. Relevant
molecular test results, including those on isocitrate dehydrogenase (IDH), O6-methylguanine-DNA
methyltransferase (MGMT) and telomere reverse transcriptase (TERT), were collected.
The mean relative cerebral blood volume of DSC-MRI and histogram parameters derived
from DCE-MRI (volume transfer coefficient (Ktrans), fractional volume of the extravascular extracellular space (Ve), fractional blood plasma volume (Vp), rate constant between the extravascular extracellular space and blood plasma (Kep) and area under the curve (AUC)) were calculated. Differences in each parameter between
gliomas with different expression states (IDH, MGMT, and TERT) were evaluated. The
diagnostic efficiency of each parameter was analyzed. The overall survival of all
patients was assessed.
Results
The 10th percentile AUC (AUC = 0.830, sensitivity = 0.78, specificity = 0.80), the
90th percentile Ve (AUC = 0.816, sensitivity = 0.84, specificity = 0.79), and the mean Kep (AUC = 0.818, sensitivity = 0.76, specificity = 0.78) provided the highest differential
efficiency for IDH, MGMT, and TERT, respectively. Kaplan-Meier curves showed a significant
difference between subjects with a 10th percentile AUC higher or lower than 0.028
(log-rank = 7.535; p = 0.006) for IDH and between subjects with different 90th percentile Ve values (log-rank = 6.532; p = 0.011) for MGMT.
Conclusion
Histogram DCE-MRI demonstrates good diagnostic performance in identifying different
molecular types and for the prognostic assessment of glioma.
Key Words
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Article Info
Publication History
Published online: January 23, 2020
Accepted:
December 7,
2019
Received in revised form:
December 6,
2019
Received:
September 25,
2019
Identification
Copyright
© 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
