Integration of conventional MRI and diffusion-weighted imaging for differential diagnosis of high-grade gliomas and metastatic brain tumors
Abstract
Brain tumors, particularly high-grade gliomas (HGG) and solitary brain metastases (SBM), often present with overlapping clinical and imaging features, complicating accurate diagnosis and management. Conventional MRI is essential for evaluation, while diffusion-weighted imaging (DWI) offers additional microstructural information. To determine whether combining conventional brain MRI and DWI can improve differentiation between SBM and HGG, with the goal of enhancing diagnostic accuracy and informing therapeutic decision-making. This cross-sectional study enrolled 126 consecutive patients with a single contrast-enhancing intra-axial brain lesion (HGG or SBM) through 2023; cases with nondiagnostic MRI were excluded. A blinded neuroradiologist reviewed conventional MRI and DWI/ADC on PACS, recording tumor location (supra-/infratentorial), morphology (shape, margin, enhancement), peritumoral features (edema and infiltration indices), and diffusion parameters correlated with post-contrast images. Inter-rater reliability was assessed on a random subset by a second neuroradiologist. Among 126 patients (65 men, 61 women; mean age 63.1 years), 53.2 % had HGG and 46.8 % had SBM. HGGs more frequently exhibited irregular shape (70.7 %), ill-defined margins (80 %), heterogeneous enhancement (71.1 %), and greater peri-tumoral infiltration. The tumor edema index differed between groups (median 1.5 for HGG vs. 2.5 for SBM) and demonstrated strong diagnostic performance (AUC = 0.918, p < 0.001). Integrating conventional MRI with DWI substantially improves differentiation between HGG and SBM, thereby supporting more accurate diagnosis and better-informed treatment planning.
Fields of Study & Subjects
Glioma Diagnosis and Treatment
MRI in cancer diagnosis
Advanced Neuroimaging Techniques and Applications
Publication details
- Journal / source
- Current Radiopharmaceuticals
- Publisher
- Elsevier BV
- Volume
- 19
- Issue
- 2
- Pages
- 100022–100022
- Publication date
- 2 February 2026
- Language
- EN