The Role of 3D-pCASL MRI in the Differential Diagnosis of Glioblastoma and Brain Metastases

Differentiation of Glioblastoma and Solitary Brain Metastasis Using Brain-Tumor Interface Radiomics Features Based on MR Images: A Multicenter Study

RATIONALE AND OBJECTIVES

Glioblastoma (GBM) and solitary brain metastasis (SBM) exhibit similar radiomics features on magnetic resonance imaging (MRI), yet their treatment strategies and prognoses significantly differ. Therefore, accurate differentiation between these two types of tumors is crucial for clinical decision-making. This study aims to establish and validate an efficient diagnostic model based on the radiomic features of the T1-weighted contrast-enhanced (T1CE) sequence in the 10 mm brain-tumor interface region to achieve precise differentiation between GBM and SBM.

METHODS

This study retrospectively collected contrast-enhanced T1-weighted imaging data from 226 GBM patients and 206 SBM patients at three centers between January 2010 and October 2024. Samples from centers 1 and 2 were used as the training set, while samples from center 3 were used as the test set. Two observers manually delineated the tumor edges on the T1CE images layer by layer to obtain the Region of Interest (ROI) covering the entire tumor volume. A 10 mm brain-to-tumor interface (BTI) was extracted using Python code. Radiomic features were extracted from the 10 mm BTI region, followed by feature selection and model construction. Finally, SHAP (SHapley Additive exPlanations) was used to visualize the model. Three radiologists with 2, 6, and 18 years of diagnostic experience independently evaluated the test set samples without knowing the patient information or pathology results, establishing three diagnostic models. The DeLong test was used to compare these models with the radiomic model.

RESULTS

Ultimately, ten radiomic features were used for modeling. The model established using the logistic regression (LR) algorithm had an AUC of 0.893 on the training set and 0.808 on the test set. The AUCs of the three radiologists with different diagnostic experiences on the test set were 0.699, 0.740, and 0.789, respectively, all lower than that of the radiomic model. The DeLong test showed that ModelBTI performed significantly better than Doctor 1 (p<0.05) in the test set, but there was no statistically significant difference in performance between ModelBTI and Doctors 2 and 3.

CONCLUSION

The radiomic model constructed based on the 10 mm brain-tumor interface can effectively differentiate between GBM and SBM, capturing tumor heterogeneity from a new perspective, thereby significantly improving diagnostic performance and providing assistance for clinical diagnosis.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplemental material, further inquiries can be directed to the corresponding authors.

Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques

BACKGROUND

Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear.

PURPOSE

To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation.

STUDY TYPE

Systematic review and meta-analysis.

POPULATION

Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488).

FIELD STRENGTH/SEQUENCE

Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T.

ASSESSMENT

Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs.

STATISTICAL TESTS

Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant.

RESULTS

HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08).

DATA CONCLUSION

ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible.

LEVEL OF EVIDENCE: 1

TECHNICAL EFFICACY

Stage 2.

Distinguishment between high-grade gliomas and solitary brain metastases in peritumoural oedema: quantitative analysis using synthetic MRI at 3 T

AIM

To investigate the efficacy of synthetic magnetic resonance imaging (MRI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs) in peritumoural oedema.

MATERIALS AND METHODS

Thirty-five patients with HGGs and 25 patients with SBMs were recruited and scanned using synthetic MRI using a 3 T scanner. Two radiologists measured synthetic MRI-derived relaxation values independently (T1, T2, proton density [PD]) in the peritumoural oedema, which was used to generate quantitative metrics before (T1native, T2native, and PDnative) and after (T1post, T2post, and PDpost) contrast agent injection. Student's t-test or the Mann-Whitney U-test was performed to detect statistically significant differences in the aforementioned metrics in peritumoural oedema between HGGs and SBMs. The receiver operating characteristic (ROC) curves were plotted to evaluate the efficacy of each metric in distinguishing the two groups, and the areas under the curves (AUCs) were compared pairwise by performing the Delong test.

RESULTS

The mean T1native, T2native, and T1post values in the peritumoural oedema of HGGs were significantly lower compared with SBMs (all p<0.05). The T1post value had a higher AUC (0.843) in differentiating HGGs and SBMs than all other individual metrics (all p<0.05). The combined T1native, T2native, and T1post model had the best distinguishing performance with an AUC, sensitivity, and specificity of 0.987, 94.3%, and 100%, respectively.

CONCLUSIONS

Synthetic MRI may be a potential supplement to the preoperative diagnosis of HGGs and SBMs in clinical practice, as the synthetic MRI-derived tri-parametric model in the peritumoural oedema showed significantly improved diagnostic performance in distinguishing HGGs from SBMs.

Arterial Spin Labeling and Amide Proton Transfer Imaging can Differentiate Glioblastoma from Brain Metastasis: A Systematic Review and Meta-Analysis

BACKGROUND

Currently, arterial spin labeling (ASL) and amide proton transfer (APT) imaging have shown potential for distinguishing glioblastoma from brain metastases. Thus, a meta-analysis was conducted to investigate this further.

METHODS

An extensive and comprehensive search was conducted in 6 English and Chinese databases according to predefined inclusion and exclusion criteria, encompassing data up to July 2023. Data from eligible literature were extracted, and bivariate models were employed to calculate pooled sensitivities, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve.

RESULTS

The meta-analysis included 11 articles. For ASL, the pooled sensitivity was 0.77 (95% confidence interval [CI], 0.63-0.87), and the pooled specificity was 0.87 (95% CI, 0.77-0.93). The pooled PLR was 5.89 (95% CI, 2.97-11.69), the pooled NLR was 0.26 (95% CI, 0.15-0.47), the pooled DOR was 22.33 (95% CI, 6.89-72.34), and AUC was 0.90 (95% CI, 0.87-0.92). For APT imaging, the pooled sensitivity was 0.78 (95% CI, 0.70-0.85), and the pooled specificity was 0.86 (95% CI, 0.77-0.92). The pooled PLR was 5.51 (95% CI, 3.24-9.37), the pooled NLR was 0.25 (95% CI, 0.17-0.37), the pooled DOR was 21.99 (95% CI, 10.28-47.03), and the AUC was 0.90 (95% CI, 0.87-0.92).

CONCLUSIONS

This meta-analysis suggest that both ASL and APT imaging exhibit high accuracy in distinguishing between glioblastoma and brain metastasis.

Perifocal Zone of Brain Gliomas: Application of Diffusion Kurtosis and Perfusion MRI Values for Tumor Invasion Border Determination

(1) Purpose: To determine the borders of malignant gliomas with diffusion kurtosis and perfusion MRI biomarkers. (2) Methods: In 50 high-grade glioma patients, diffusion kurtosis and pseudo-continuous arterial spin labeling (pCASL) cerebral blood flow (CBF) values were determined in contrast-enhancing area, in perifocal infiltrative edema zone, in the normal-appearing peritumoral white matter of the affected cerebral hemisphere, and in the unaffected contralateral hemisphere. Neuronavigation-guided biopsy was performed from all affected hemisphere regions. (3) Results: We showed significant differences between the DKI values in normal-appearing peritumoral white matter and unaffected contralateral hemisphere white matter. We also established significant (p < 0.05) correlations of DKI with Ki-67 labeling index and Bcl-2 expression activity in highly perfused enhancing tumor core and in perifocal infiltrative edema zone. CBF correlated with Ki-67 LI in highly perfused enhancing tumor core. One hundred percent of perifocal infiltrative edema tissue samples contained tumor cells. All glioblastoma samples expressed CD133. In the glioblastoma group, several normal-appearing white matter specimens were infiltrated by tumor cells and expressed CD133. (4) Conclusions: DKI parameters reveal changes in brain microstructure invisible on conventional MRI, e.g., possible infiltration of normal-appearing peritumoral white matter by glioma cells. Our results may be useful for plotting individual tumor invasion maps for brain glioma surgery or radiotherapy planning.

[Radiological biomarkers of brain gliomas]

The most important objective of modern neuroimaging is comparison of data on genotype and phenotype of brain gliomas. Radiogenomics as a new branch of science is devoted to searching for such relationships based on MRI and PET/CT parameters. The 2021 WHO classification of tumors of the central nervous system poses the most important tasks for physicians in assessment of biological behavior of tumors and their response to combined treatment. The review demonstrates the possibilities and prospects of preoperative MRI and PET/CT with amino acids in assessing the genetic profile of brain gliomas.