The Potential Role of Peritumoral Apparent Diffusion Coefficient Evaluation in Differentiating Glioblastoma and Solitary Metastatic Lesions of the Brain

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

BACKGROUND

This study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases.

METHODS

A retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC50Hz and ADC0Hz), we generated maps of the ADC change (cADC): ADC50Hz - ADC0Hz and the relative ADC change (rcADC): (ADC50Hz - ADC0Hz)/ ADC0Hz × 100 (%).

RESULTS

The mean and the fifth and 95th percentile values of each parameter in enhancing and peritumoral regions were compared between glioblastomas and metastases. The area under the receiver operating characteristic curve (AUC) values of the best discriminating indices were compared. In enhancing regions, none of the indices of ADC0Hz and ADC50Hz showed significant differences between metastases and glioblastomas. The mean cADC and rcADC values of metastases were significantly higher than those of glioblastomas (0.24 ± 0.12 × 10-3mm2/s vs. 0.14 ± 0.03 × 10-3mm2/s and 23.3 ± 9.4% vs. 14.0 ± 4.7%; all p < 0.01). In peritumoral regions, no significant difference in all ADC indices was observed between metastases and glioblastomas. The AUC values for the mean cADC (0.877) and rcADC (0.819) values in enhancing regions were significantly higher than those for ADC0Hz5th (0.595; all p < 0.001).

CONCLUSIONS

The time-dependent diffusion MRI parameters may be useful for differentiating brain metastases from glioblastomas.

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.

Efficacy evaluation of contrast-enhanced magnetic resonance imaging in differentiating glioma from metastatic tumor of the brain and exploration of its association with patients’ neurological function

Objective

To determine the efficacy of contrast-enhanced MRI in differentiating glioma (GL) from the metastatic tumor of the brain (MTB) and its association with patients’ neurological function.

Methods

A retrospective analysis was conducted on 49 cases of pathologically confirmed GL and 42 cases of MTB admitted between April 2019 and January 2022. All patients were examined by a set of MRI sequences that included T1WI, T2WI, FLAIR, and DWI. The values of fractional anisotropy (FA), apparent diffusion coefficient (ADC), and operation coefficient (K^trans) were calculated by taking the tumor parenchyma area, cystic area, and peritumor edema area as the regions of interest (ROIs). And according to the Mini-mental state examination (MMSE) results, the contrast-enhanced MRI with patients’ neurological dysfunction was observed.

Results

The clinical symptoms and MRI findings of MTB and GL were basically the same, mainly showing neurological symptoms. The tumor parenchyma area and cystic area were mainly located in the tumor periphery and tumor central area, respectively, while the peritumor edema area was widely distributed, showing an irregular patchy edema zone. Contrast-enhanced scans suggested an obvious enhancement in the tumor parenchymal area, presenting with nodular and annular enhancement, but no enhancement in the tumor cystic and peritumor edema areas. There was no difference between GL and MTB in FA values of tumor cystic area and peritumor edema area (P > 0.05), but the FA value of the parenchyma area of GL was higher (P < 0.05). Besides, GL and MTB showed no difference in ADC and K^trans values (P > 0.05), while the former presented lower ADC values and higher K^trans values of the peritumor edema area than the latter (P < 0.05). In patients with GL and MTB, the FA and K^trans values of all ROIs in those with neurological dysfunction were higher compared with those without neurological dysfunction, while the ADC values were lower (P < 0.05).

Conclusion

Contrast-enhanced MRI of peritumor edema area can effectively distinguish GL from MTB, and improve the accuracy of early clinical screening, thus providing more reliable life security for patients.

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.