Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest

Abstract

Abstract. The present study aimed to explore the role of texture analysis with apparent diffusion coefficient (ADC) maps based on different regions of interest (ROI) in determining glioma grade. Thirty patients with glioma underwent diffusion-weighted imaging (DWI). ADC values were determined from the following three ROIs: i) whole tumor; ii) solid portion; and iii) peritumoral edema. Texture features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) using the non-parametric Wilcoxon rank-sum test or the unpaired Student's t-test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for inhomogeneity values in discrimination of HGGs from LGGs. With a spearman rank correlation model, the aforementioned ADC inhomogeneity values were correlated with the Ki-67 labeling index. With whole tumor ROI, inhomogeneity values proved to be significantly different between HGGs and LGGs (P<0.001). With solid portion ROI, inhomogeneity and median values showed significant difference between HGGs and LGGs (P=0.001 and P=0.043, respectively). With peritumoral edema ROI, entropy and edema volume demonstrated positive results (P=0.016, P<0.001). The whole tumor inhomogeneity parameter performed with better diagnostic accuracy (P=0.048) than selecting the solid portion ROI. The association between inhomogeneity and Ki-67 labeling index was significantly positive in whole tumor and solid portion ROI (R=0.628, P<0.001 and R=0.470, P=0.009). Texture analysis of DWI based on different ROI can provide various significant parameters to evaluate tumor heterogeneity, which were correlated with tumor grade. Particularly, the inhomogeneity value derived from whole tumor ROI provided high diagnostic value and predicting the status of tumor proliferation. IntroductionGliomas are the most common type of primary brain tumor. The World Health Organization (WHO) classifies gliomas into grades I-IV, where I and II are low-grade gliomas (LGGs) and III and IV are high-grade gliomas (HGGs) (1). Determining the correct grade of the tumor is of great importance as it dictates the management and prognosis for the patient (2). HGGs are managed with radical resection and with adjuvant radiotherapy and/or chemotherapy, whereas LGGs are very slow growing and can undergo curative resection and have considerably better prognosis (3). The current gold standard for grading gliomas is histopathological assessment by stereotactic brain biopsy, which is an invasive procedure. Particularly with gliomas, the potential to increase clinical utility of imaging as a non-invasive technique to accurately ascertain tumor grade is gaining a lot of attention (4).Advanced MR imaging techniques such as diffusion-weighted imaging (DWI) and its estimated apparent diffusion coefficient (ADC) can probe the pathological changes in glioma providing abundant important information that is not apparent on conventional imaging (5-7). ADC, which reflects the volume of the extracellular water compartment, is sensitive ...

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

“…From the 394 interpretation of the features and the results described above, it could be deduced that 395 LGGs had a more heterogeneous texture than HGGs, specifically in the T 2 contrasts; 396 and HGGs had a more heterogeneous texture than LGGs, specifically in the T 1Gd 397 contrasts; in both cases studying the NCR/NET region. Several works have reported 398 models whose main classification variable was heterogeneity of gliomas [18,23,25,[47][48][49]. 399 For example, through texture analysis applied on diffusion tensor imaging [25,49] and 400 diffusion kurtosis imaging [49] maps, diverse features that characterized the 401 heterogeneity of gliomas indicated an increased heterogeneity for higher grade gliomas 402 compared to lower grade gliomas.…”

“…Several works have reported 398 models whose main classification variable was heterogeneity of gliomas [18,23,25,[47][48][49]. 399 For example, through texture analysis applied on diffusion tensor imaging [25,49] and 400 diffusion kurtosis imaging [49] maps, diverse features that characterized the 401 heterogeneity of gliomas indicated an increased heterogeneity for higher grade gliomas 402 compared to lower grade gliomas. Moreover, Kin et al [47] studied the texture matrix 403 called Grey Level Co-occurrence Matrix (GLCM) of contrast enhanced T1 MR and 404 ADC maps and reported higher values of entropy (or non-uniformity) as well as reduced 405 values of homogeneity for HGGs when these were compared to LGGs.…”

Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis

“…Twenty-nine articles were irrelevant and three could not provide sufficient data to construct the 2×2 table. According to the inclusion criteria, six studies8 11–15 were retrieved. The study selection process is shown in figure 1.…”

“…Some reports have suggested that TA holds promise in the field of oncology diagnosis, including quantifying tumour heterogeneity and tumour grading 10 11. Until now, some reports have been published regarding tumour heterogeneity in glioma using MRI TA 8 11–15. However, these studies were inconclusive because of insufficient samples and different diagnostic algorithms.…”

Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma

“…"Radiomics", which is de ned as the high-throughput extraction of image features from radiographic images, has potential to provide a detailed pre-operative evaluation method of tumor heterogeneity. [11][12][13][14][15][16][17] This method of imaging analysis utilizes algorithms to derive image texture. At present, the most commonly used image texture analysis methods are rst and second order statistical method analyses of image pixels and their neighborhood gray level.…”