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Journal of Neuro-Oncology

, Volume 139, Issue 1, pp 61–68 | Cite as

Diagnostic performance of apparent diffusion coefficient parameters for glioma grading

  • Qun Wang
  • JiaShu Zhang
  • Xinghua Xu
  • XiaoLei Chen
  • BaiNan Xu
Clinical Study

Abstract

This study was to evaluate the diagnostic performance of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) parameters derived from diffusion tensor imaging in the differentiation between grade II and III gliomas. The records of 60 patients (30 women, 30 men; mean age, 45.4 years) suspected of having gliomas who underwent an ADC image-guided stereotactic biopsy were retrospectively reviewed. The values of FA and ADC were measured, and the sensitivity, specificity, accuracy and area under the curve (AUC) of those parameters were calculated based on the receiver operating characteristic curve analysis. A predictive diagnostic equation was also constructed and evaluated. Significant differences in minimum ADC values were found in the quantitative analysis between the grade III and II glioma groups. The sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), accuracy and AUC for identifying grade III and II gliomas at the optimum cut-off value of 0.895 × 10−3 mm2/s of minimum ADC were 81.0, 89.1, 77.3, 91.1, 86.6 and 0.87, respectively. The predictive diagnostic equation was superior to the single minimum ADC indicator with a sensitivity of 90.5%, a specificity of 84.8%, a PPV of 73.1%, an NPV of 95.1%, and an accuracy of 86.6%, respectively. The study provides evidence that minimum ADC values have a superior diagnostic performance in differentiating grade III and II gliomas, and the predictive diagnostic equation may be helpful in the differentiation.

Keywords

Diffusion magnetic resonance imaging Apparent diffusion coefficient Brain neoplasms Biopsy 

Abbreviations

ADC

Apparent diffusion coefficient

AU

Arbitrary unit

AUC

Area under the curve

CI

Confidence intervals

DTI

Diffusion tensor imaging

DWI

Diffusion-weighted imaging

FA

Fractional anisotropy

FN

False negative

FP

False positive

HGG

High-grade glioma

LGG

Low-grade glioma

MRI

Magnetic resonance imaging

MRS

Magnetic resonance spectroscopy

NOS

Not otherwise specified

NPV

Negative predictive values

PPV

Positive predictive values

ROC

Receiver operating characteristic curve

SD

Standard deviation

SEN

Sensitivity

SPE

Specificity

TN

True negative

TP

True positive

Notes

Acknowledgements

The scientific guarantor of this publication is Xiaolei Chen, PHD. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. This study has received funding by Hospital Young Doctor Funding Plan of Chinese PLA General Hospital (Grant Number 15KMM19), Hospital Clinical Sponsor Foundation Plan of Chinese PLA General Hospital (Grant Number 2016FC-TSYS-1023). One of the authors (Xinghua Xu) has significant statistical expertise. Institutional review board approval and written informed consent were obtained.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of NeurosurgeryChinese PLA General HospitalBeijingChina

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