September 27, 2025

Value of the diagnostic algorithm in differentiating glioblastoma from solitary brain metastasis and primary central nervous system lymphomas using Dynamic susceptibility contrast-enhanced MRI (DSCE-MRI) and Proton magnetic resonance spectroscopy (1H-MRS).

Key Points

The diagnostic algorithm achieved 100% accuracy for glioblastoma and primary lymphomas, indicating its reliability for critical tumor differentiation.
Statistical analysis showed significant differences in imaging indices between glioblastoma, solitary metastasis, and central nervous system lymphomas.
Using rCBVe/n, rCBFt/n and other metrics, the algorithm demonstrated 100% sensitivity and 98.9% specificity, very high diagnostic performance.
This non-invasive imaging method may enhance pre-treatment diagnosis and prognosis, improving patient outcomes.

Abstract

Our study aims to differentiate and develop the diagnostic algorithm for glioblastoma (GBM), solitary brain metastasis (SM), and primary central nervous system lymphomas (PCNSLs) using Dynamic susceptibility contrast-enhanced MRI (DSCE-MRI) and Proton magnetic resonance spectroscopy (1H-MRS). This retrospective study included 91 patients (51 GBM, 18 SM, and 22 PCNSLs) who underwent preoperative imaging with a standard 3T MRI brain tumor protocol, including conventional Magnetic Resonance Imaging (cMRI), DSCE-MRI, and 1H-MRS. All patients underwent surgery or stereotactic biopsy with histopathological confirmation. On DSCE-MRI, the ratios of tumor regions (t) and peritumoral regions (e) to normal white matter (n) in CBV and CBF maps were analyzed, including rCBVt, rCBFt, rCBVt/n, rCBFt/n; rCBVe, rCBFe, rCBVe/n, and rCBFe/n. On 1H-MRS, metabolite ratios of the tumor and peritumoral regions were evaluated, comprising tCho/NAA, tCho/Cr, pCho/NAA, and pCho/Cr. Conducted the statistical analysis using the Fisher test or Chi-square test, One-way ANOVA tests, and decision tree analysis. The differences in indices between the two imaging modalities were statistically significant in differentiating tumor types: (1) GBM vs. SM: the values of rCBVe, rCBVe/n, rCBFt, rCBFe, rCBFt/n, tCho/Cr, tCho/NAA, eCho/Cr, and eCho/NAA were significantly higher in GBM compared to SM (p < 0.05). (2) GBM vs. PCNSLs: the values of rCBVt, rCBVe, rCBVt/n, rCBVe/n, rCBFt, rCBFe, rCBFt/n, rCBFe/n, eCho/Cr, and eCho/NAA were significantly higher in GBM compared to PCNSLs (p < 0.001). (3) SM vs. PCNSLs: The values of rCBVt, rCBVt/n, rCBFt, and rCBFe/n of SM were significantly higher, while tCho/NAA of SM were lower compared to PCNSLs (p < 0.001). The diagnostic algorithm using rCBVe/n, rCBFt/n, rCBVt, rCBVe, and tCho/NAA achieved 100% accuracy in diagnosing GBM and PCNSLs, and 94.7% for SM, with a misclassification risk estimate of 2.1%, the sensitivity of 100%, specificity of 98.9%, and an AUC of 0.993. The values obtained from DSCE, 1H-MRS, and the diagnostic model play a crucial role in differentiating GBM, SM, and solitary PCNSLs. Our DSCE and 1H-MRS-based algorithm accurately differentiates GBM, SM, and PCNSLs, achieving 100% sensitivity and 98.9% specificity. This non-invasive method enhances pre-treatment diagnosis and prognosis, improving diagnostic quality for patients.

KI fragen

Bookmark