Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images

Introduction: Based on the tumor’s growth potential and aggressiveness, glioma is most often classified into low or high-grade groups. Traditionally, tissue sampling is used to determine the glioma grade. The aim of this study is to evaluate the efficiency of the Laplacian Re-decomposition (LRD) medical image fusion algorithm for glioma grading by advanced magnetic resonance imaging (MRI) images and introduce the best image combination for glioma grading. Material and methods: Sixty-one patients (17 low-grade and 44 high-grade) underwent Susceptibility-weighted image (SWI), apparent diffusion coefficient (ADC) map, and Fluid attenuated inversion recovery (FLAIR) MRI imaging. To fuse different MRI image, LRD medical image fusion algorithm was used. To evaluate the effectiveness of LRD in the classification of glioma grade, we compared the parameters of the receiver operating characteristic curve (ROC). Results: The average Relative Signal Contrast (RSC) of SWI and ADC maps in high-grade glioma are significantly lower than RSCs in low-grade glioma. No significant difference was detected between low and high-grade glioma on FLAIR images. In our study, the area under the curve (AUC) for low and high-grade glioma differentiation on SWI and ADC maps were calculated at 0.871 and 0.833, respectively. Conclusions: By fusing SWI and ADC map with LRD medical image fusion algorithm, we can increase AUC for low and high-grade glioma separation to 0.978. Our work has led us to conclude that, by fusing SWI and ADC map with LRD medical image fusion algorithm, we reach the highest diagnostic accuracy for low and high-grade glioma differentiation and we can use LRD medical fusion algorithm for glioma grading.

Proceedings of the 2021 National Toxicology Program Satellite Symposium

The 2021 annual National Toxicology Program (NTP) Satellite Symposium, entitled “Pathology Potpourri,” was the 20th anniversary of the symposia and held virtually on June 25th, in advance of the Society of Toxicologic Pathology’s 40th annual meeting. The goal of this symposium was to present and discuss challenging diagnostic pathology and/or nomenclature issues. This article presents summaries of the speakers’ talks along with select images that were presented to the audience for voting and discussion. Various lesions and topics covered during the symposium included differentiation of canine oligodendroglioma, astrocytoma, and undefined glioma with presentation of the National Cancer Institute’s updated diagnostic terminology for canine glioma; differentiation of polycystic kidney, dilated tubules and cystic tubules with a discussion of human polycystic kidney disease; a review of various rodent nervous system background lesions in control animals from NTP studies with a focus on incidence rates and potential rat strain differences; vehicle/excipient-related renal lesions in cynomolgus monkeys with a discussion on the various cyclodextrins and their bioavailability, toxicity, and tumorigenicity; examples of rodent endometrial tumors including intestinal differentiation in an endometrial adenocarcinoma that has not previously been reported in rats; a review of various rodent adrenal cortex lesions including those that represented diagnostic challenges with multiple processes such as vacuolation, degeneration, necrosis, hyperplasia, and hypertrophy; and finally, a discussion of diagnostic criteria for uterine adenomyosis, atypical hyperplasia, and adenocarcinoma in the rat.

Feasibility of generalised DKI approach for brain glioma grading

Purpose: An accurate differentiation of brain glioma grade constitutes an important clinical issue. Powerful non-invasive approach based on diffusion MRI has already demonstrated its feasibility in glioma grade stratification. However, the conventional diffusion tensor (DTI) and kurtosis imaging (DKI) demonstrated moderate sensitivity and performance in glioma grading. In the present work, we apply generalised DKI (gDKI) approach in order to assess its diagnostic accuracy and potential application in glioma grading. Methods: Diffusion scalar metrics were obtained from 50 patients with different glioma grades confirmed by histological tests following biopsy or surgery. All patients were divided into two groups with low- and high-grade gliomas as II grade versus III and IV grades, respectively. For a comparison, trained radiologists segmented the brain tissue into three regions with solid tumour, oedema, and normal appearing white matter. For each region we estimated the conventional and gDKI metrics including DTI maps. Results: We found high correlations between DKI and gDKI metrics in high-grade glioma. Further, gDKI metrics enabled introduction of a complementary measure for glioma differentiation based on correlations between the conventional and generalised approaches. Both conventional and generalised DKI metrics showed quantitative maps of tumour heterogeneity and oedema behaviour. gDKI approach demonstrated largely similar sensitivity and specificity in low-high glioma differentiation as in the case of conventional DKI method. Conclusion: The generalised diffusion kurtosis imaging enables differentiation of low and high grade gliomas at the same level as the conventional DKI. Additionally, gDKI exhibited higher tissue contrast between tumour and healthy tissue and, thus, may contribute as a complementary source of information on tumour heterogeneity.