Background

Gliomas are the most common primary brain tumors. They are stratified into low-grade gliomas (LGG, classified as World Health Organization [WHO] grade I–II) and high-grade gliomas (HGG, classified as WHO grade III–IV)。Tumor grading is important for making treatment decisions and predicting prognosis. HGG is treated by radical resection followed by adjuvant radiotherapy and/or chemotherapy, while LGG grows very slowly and can be radically resected with a good prognosis. The main difference between conventional enhanced MRI and DCE-MRI is the number of images acquired after contrast injection. In conventional MRI, only one set of images is obtained a few minutes after gadolinium contrast injection. In DCE-MRI, continuous dynamic imaging provides multiple images that can be used to calculate a signal intensity curve at different points before and after contrast injection. This DCE kinetic curve reveals the transport of gadolinium contrast to the lesion, as well as its distribution and clearance. Texture analysis is a method of quantifying the spatial distribution of image intensity. It has the potential to be widely used for tumor diagnosis, tumor heterogeneity quantification, separation of tumor from surrounding tissues, tumor grading and classification, and prediction of treatment response and survival. The present study investigated the application of texture analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in different regions of interest (ROIs) in glioma grading.

Methods

Fifty patients with high-grade glioma (HGG, World Health Organization [WHO] grade III–IV) and 50 with low-grade glioma (LGG; WHO grade I–II) were included. All scans were performed on a clinical 3T MR system (MAGNETOM Skyra, Siemens AG). A DCE T1-weighted 3D gradient echo sequence was used for volumetric interpolated breath-hold examinations. Prior to the DCE scan, sequential scanning was performed at two different flip angles (2° and 15°) to generate T1 maps during post-processing. Gd-DTPA (GE Healthcare) was injected via an elbow vein trocar (20 G) using a power injector (Medrad, Spectris Solaris EP, USA) at a dose of 0.2 mmol/kg and a rate of 2.5 ml/s. The connecting tube was rinsed at the same flow rate with 20 ml of normal saline immediately after injection. Multi-phase transverse DCE-MRI began when the contrast injection was started. MRI data were transferred to a personal computer. All ROIs in the solid portion and whole tumor were drawn by the same investigator under the guidance of an experienced neuroimaging technologist. To assess image texture features in ROIs, GE Analysis Kit Version V3.0.1A software was used to quantify heterogeneous signals. Histogram analysis was used to calculate first-order parameters. A gray level co-occurrence matrix (GLCM) was applied to extract second-order statistical texture features. Texture analysis was performed in 3D ROIs. Parameter inhomogeneity was used for quantitative analysis. Kurtosis and inhomogeneity were compared between HGG and LGG using a nonparametric Wilcoxon rank sum tests.

Results

For both whole tumor ROI, heterogeneity was significantly different between HGG and LGG (Z = 52.11, P = 0.01 < 0.05). For the solid portion ROI, inhomogeneity was not significantly different between HGG and LGG (Z = 35.54, P = 0.07). Among the two parameters, whole tumor inhomogeneity demonstrated higher diagnostic accuracy (Tables 1 and 2). Table 3 and Figure 1 summarizes the ROC analysis results for different ROI inhomogeneities used to discriminate HGG from LGG.

Conclusions

This study demonstrates that texture analysis of DCE-MRI in different ROIs can provide important parameters for evaluating tumor heterogeneity, which is correlated with tumor grade. Inhomogeneity in the whole tumor ROI is particularly effective for discriminating HGG from LGG.

Acknowledgements

No acknowledgement found.

References

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