Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping

Abstract

BackgroundTo determine if filtration-histogram based texture analysis (MRTA) of clinical MR imaging can non-invasively identify molecular subtypes of untreated gliomas.Methods Post Gadolinium T1-weighted (T1+Gad) images, T2-weighted (T2) images and apparent diffusion coefficient (ADC) maps of 97 gliomas (54=WHO II, 20=WHO III, 23=WHO IV) between 2010 and 2016 were studied. Whole-tumor segmentations were performed on a proprietary texture analysis research platform (TexRAD, Cambridge, UK) using the software's freehand drawing tool.MRTA commences with a filtration step, followed by quantification of texture using histogram texture parameters. Results were correlated using non-parametric statistics with a logistic regression model generated.Results T1+Gad performed best for IDH typing of glioblastoma (sensitivity 91.9%, specificity 100%, AUC 0.945) and ADC for non-Gadolinium-enhancing gliomas (sensitivity 85.7%, specificity 78.4%, AUC 0.877). T2 was moderately precise (sensitivity 83.1%, specificity 78.9%, AUC 0.821). Excellent results for IDH typing were achieved from a combination of the three sequences (sensitivity 90.5%, specificity 94.5%, AUC=0.98). For discriminating 1p19q genotypes, ADC produced the best results using unfiltered textures (sensitivity 80.6%, specificity 89.3%, AUC 0.811).Conclusion Preoperative glioma genotyping with MRTA appears valuable with potential for clinical translation. The optimal choice of texture parameters is influenced by sequence choice, tumour morphology and segmentation method.

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas

“…However, such imaging characteristics may be limited in clinical application due to the unbalanced sensitivity and specificity, and highthroughput quantitative features are intensively needed to better illustrate the radiological divergences and further predict the 1p/19q status non-invasively. Previous radiomics studies using conventional MRI or advanced MRI sequences to predict 1p/19q status reached AUCs ranging from 0.68 to 0.96 (if reported, without distinguishing the training and validation dataset) (18)(19)(20)(21)(22)(23)(24)(25)(26), and our study displayed a competent result, with AUCs around 0.90 for the whole population and further elevated in IDH-mutated tumors, suggesting the capability of our signature for non-invasive 1p/19q detection. In addition, the 3D signature also displayed a balanced sensitivity and specificity, which compensated for the disequilibrium of visual characteristics.…”

“…studies of glioma have investigated the association between selected radiomics features and WHO grading, molecular characteristics, clinical manifestations, and patient prognosis (16)(17)(18). A few studies have involved the non-invasive prediction of 1p/19q status through a radiomics approach but display only moderate prediction value (18)(19)(20)(21)(22)(23)(24)(25)(26), and further investigation is needed to establish a reliable radiomics signature. In addition, previous studies were conducted using MR images acquired with diverse spacing (ranging from 1 to 5-6 mm for contrastenhanced T1 [CE-T1]-weighted images), and whether such differences would influence the performance of the prediction model remains to be explored.…”

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

“…Quantitative analysis of the filtered pixel values is conducted after the image-filtration step. The parameters include mean of positive pixel values, mean intensity, SD, entropy, skewness and kurtosis 25 26. Next, the AUC of the parameters to distinguish tumour grades were calculated by receiver operating characteristic curve analysis.…”

Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts

“…The ROI segmentation employed in this pilot study comprised a single largest cross-section-based delineation instead of a multislice or three-dimensional volume analysis. However, previous studies using the filtration-histogram technique have demonstrated the comparison of single-slice vs. multislice/volume analysis on computer tomography (CT) in primary colorectal cancer for prognostication [42], as well as on MRI in gliomas for IDH versus wild-type differentiation [43]. Interestingly the analysis demonstrated that single-slice analysis was significant in predicting prognosis in colorectal cancer on CT [42] and IDH vs. wild-type differentiation in gliomas on MRI [43], and it was comparable to multislice/volume analysis.…”

“…However, previous studies using the filtration-histogram technique have demonstrated the comparison of single-slice vs. multislice/volume analysis on computer tomography (CT) in primary colorectal cancer for prognostication [42], as well as on MRI in gliomas for IDH versus wild-type differentiation [43]. Interestingly the analysis demonstrated that single-slice analysis was significant in predicting prognosis in colorectal cancer on CT [42] and IDH vs. wild-type differentiation in gliomas on MRI [43], and it was comparable to multislice/volume analysis. It is not, therefore, clear if there is any "significant" added-value of undertaking multislice/volumetric analysis, which not only entails increased analysis time (barrier to adoption in a busy clinic) but also increased operator variability associated with multislice/volume analysis.…”