3 years ago

The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading

Qiuping Gui, Huiguang He, Mengqi Liu, Zhiye Chen, Xin Lou, Lin Ma, Peng Zhou, Yan Wang, Bin Lv, Yulin Wang

Abstract

Objectives

To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading.

Methods

Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform.

Results

There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2.

Conclusions

HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice.

Key Points

HFP shows positive correlation with neuroepithelial tumour grading.

HFP presents a good diagnostic efficacy for LGG and HGG.

HFP is helpful in the selection of brain tumour boundary.

Publisher URL: https://link.springer.com/article/10.1007/s00330-017-4899-4

DOI: 10.1007/s00330-017-4899-4

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