Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: efficacy in preoperative grading

Abstract

The preoperative grading of gliomas, which is critical for guiding therapeutic strategies, remains unsatisfactory. We aimed to retrospectively assess the efficacy of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in the grading of gliomas. Forty-two newly diagnosed glioma patients underwent conventional MR imaging, DWI, and contrast-enhanced MR imaging. Parameters of apparent diffusion coefficient (ADC), slow diffusion coefficient (D), fast diffusion coefficient (D*), and fraction of fast ADC (f) were generated. They were tested for differences between low- and high-grade gliomas based on one-way ANOVA. Receiver-operating characteristic (ROC) analyses were conducted to determine the optimal thresholds as well as the sensitivity and specificity for grading. ADC, D, and f were higher in the low-grade gliomas, whereas D* tended to be lower (all P<0.05). The AUC, sensitivity, specificity and the cutoff value, respectively, for differentiating low- from high-grade gliomas for ADC, D and f, and differentiating high- from low-grade gliomas for D* were as follows: ADC, 0.926, 100%, 82.8%, and 0.7 × 10−3 mm2/sec; D, 0.942, 92.3%, 86.2%, and 0.623 × 10−3 mm2/sec; f, 0.902, 92.3%, 86.2%, and 35.3%; D*, 0.798, 79.3%, 84.6%, and 0.303 × 10−3 mm2/sec. The IVIM DWI demonstrates efficacy in differentiating the low- from high-grade gliomas.

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

“…MB‐DWI images were processed by using the intravoxel incoherent motion (IVIM) imaging model in NordicICE using the same protocol as in previous publications, which yielded slow ADC (ie, D ), fast ADC (ie, D* ), slow fractional ADC (ie, 1‐f ), and fast fractional ADC (ie, f ) by using Eq. . Chi‐square maps were yielded by the chi‐square test on the sum of squared errors from the least‐squares fitting. where S 0 and S b are the signal intensities at a b‐value of 0 sec/mm 2 and each b‐value other than 0 sec/mm 2 , respectively.…”

“…Both conventional (including T 1 ‐ and T 2 ‐weighted images) and advanced sequences, including dynamic contrast imaging (DCE), diffusion‐weighted imaging (DWI), and arterial spinal labeling (ASL) have been applied to preoperatively classify glioma. Although it is generally reported that advanced sequences are superior to conventional ones in classifying glioma grades, controversies still exist about the diagnostic accuracy of MRI . The major reason is that only a small number of MRI attributes from the limited MRI modalities were utilized, leaving a huge amount of valuable multimodal image information unused, not to mention reflecting the heterogeneity of gliomas.…”

“…Generally, a set of first‐ and second‐order texture attributes based on varied analyzing strategies were applied to reflect specific image heterogeneity. For example, Global texture attributes, as the first‐order image texture attributes, generally refer to those statistical histogram indictors and reflect the gray‐level distribution within the whole tumor region . Second‐order texture attributes were usually based on different analysis matrices including the gray‐level co‐occurrence matrix (GLCM), gray‐level run‐length matrix (GLRLM), and gray‐level size‐zone matrix (GLSZM), which reflect different spatial gray‐level distribution from different aspects.…”

Quantification of apparent diffusion coefficient and relative cerebral blood volume for analysis concerning gender and age as determinant factors in glioma prospect

“…The discernment between types and grading of gliomas were based on morphological (MR) imaging and metabolic (MRS) imaging [1,8]. However, in pursuance of making comparisons between groups (males/females, younger/older than 45 years old), this research utilized ADC values and rCBV due to the specificity of microscopic alterations arising in gliomas [6,7,[15][16][17]21]. Translated into medical terms, high grade gliomas are characterized by necrotic, edematous and hypervascular tissue (increased rCBV and decreased ADC value) and that will be the foundation of establishing the prospect of gliomas.…”

“…Anterior studies were conducted either on the basis of retrospective, social or statistical methods to ascertain a possible correlation between age, gender and occurrence of brain tumors [11][12][13][14][15][16][17][18]21].…”