A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model

Abstract

Diffusion tensor imaging (DTI) has been proven to be a sophisticated and useful tool for the delineation of tumors. In the present study, we investigated the predictive role of DTI compared to other magnetic resonance imaging (MRI) techniques in combination with Ki-67 labeling index in defining tumor cell infiltration in the peritumoral regions of F98 glioma-bearing rats. A total of 29 tumor-bearing Fischer rats underwent T2-weighted imaging, contrast-enhanced T1-weighted imaging, and DTI of their brain using a 7.0-T MRI scanner. The fractional anisotropy (FA) ratios were correlated to the Ki-67 labeling index using the Spearman correlation analysis. A receiver operating characteristic curve (ROC) analysis was established to evaluate parameters with sensitivity and specificity in order to identify the threshold values for predicting tumor infiltration. Significant correlations were observed between the FA ratios and Ki-67 labeling index (r = - 0.865, p < 0.001). The ROC analysis demonstrated that the apparent diffusion coefficient (ADC) and FA ratios could predict 50% of the proliferating cells in the regions of interest (ROI), with a sensitivity of 88.1 and 81.3%, and a specificity of 86.2 and 90.2%, respectively (p < 0.001). Meanwhile, the two ratios could also predict 10% of the proliferating cells in the ROI, with a sensitivity of 82.5 and 94.9%, and a specificity of 100 and 88.9%, respectively (p < 0.001). The present study demonstrated that the FA ratios are closely correlated with the Ki-67 labeling index. Furthermore, both ADC and FA ratios, derived from DTI, were useful for quantitatively predicting the Ki-67 labeling of glioma cells.

Is There a Relationship between the Elasticity of Brain Tumors, Changes in Diffusion Tensor Imaging, and Histological Findings? A Pilot Study Using Intraoperative Ultrasound Elastography

“…There are descriptions based on cadaveric studies of the normal brain; some find a negative relationship between the shear modulus and the total number of cell nuclei in areas of the normal brain [ 19 ]. Similar results were found by Wang et al [ 27 ], who described an inverse relationship between FA and Ki-67 in a glial infiltration model in rats.…”

Perampanel Add-on to Standard Radiochemotherapy in vivo Promotes Neuroprotection in a Rodent F98 Glioma Model

“…To conduct the in vivo experiments with respect to orthotopic glioma progression and onset of tumor-related seizures, a robust animal model was needed. F98 glioma in Fischer rats is a wellestablished glioma model (Belloli et al, 2013;Schültke et al, 2018;Wang et al, 2018), but to the best of our knowledge, no data about its tumor-associated epileptiform phenotype have been published so far (Kirschstein and Köhling, 2016). The results of our studies suggest that F98 glioma led to interictal epileptiform events (e.g., spikes and spike-waves), indicating a high susceptibility to develop seizures, which was also demonstrated by video-EEG analysis.…”

Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study

“…We considered potential reasons as to why the BTI4mm ROI area demonstrated the overall best-predictive effectiveness in diagnosing invasive meningioma. Some research centered on detecting glioma microstructural heterogeneity using sophisticated MRI, which implicitly showed the infiltrating extension relative to standard white matter (30)(31)(32). Niha et al claimed that radiomics features extracted from intranodular and perinodular regions of nodules can distinguish nonsmall-cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT, and the most predictive features were found to be within an immediate distance of 5 mm from the nodule (33).…”