Validity of brain Magnetic Resonance Imaging (MRI) scoring system in differentiating glioblastoma from brain metastasis in adult patients
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Glioblastoma (GBM) and brain metastases are common primary and secondary brain tumors, respectively, often exhibiting overlapping radiological features. Histopathology remains the gold standard for diagnosis, but limitations arise in inoperable cases, necessitating alternative diagnostic methods. While advanced MRI sequences can aid in differentiation, their availability may be limited. This study aims to develop and validate a brain MRI scoring system utilizing readily available sequences to differentiate GBM from brain metastases based on histopathological confirmation accurately. Methods: This observational diagnostic test included 71 patients with histopathologically confirmed GBM (n=45) or brain metastases (n=26) who underwent brain MRI with contrast. Logistic regression analysis is used to identify significant brain MRI parameters. A scoring system was developed, and its diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Results: Eight brain MRI parameters were found to be significant: tumor number, margin, shape, size, edema-to-tumor ratio, intratumoral ADC value, peritumoral ADC value, and peritumoral FLAIR ratio. The developed scoring system demonstrated high diagnostic accuracy in differentiating GBM from brain metastases with a cutoff score of >8. Sensitivity was 95.5%, specificity was 96.1%, PPV was 97.7%, NPV was 92.6%, LR+ was 24.8, and LR- was 0.04, with an overall accuracy of 91%. Conclusion: This study successfully developed and validated a novel brain MRI scoring system that effectively differentiates GBM from brain metastases. This non-invasive tool offers a valuable adjunct to clinical decision-making, particularly in settings where invasive procedures are contraindicated or advanced MRI sequences are unavailable.
Institution: Udayana University
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Funder: Universitas Udayana
OpenAlex ID: W4414836760
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