Investigating glioma genetics through perfusion MRI: rCBV and rCBF as predictive biomarkers

Background

Brain tumors exhibit diverse genetic landscapes and hemodynamic properties, influencing diagnosis and treatment outcomes.

Purpose

To explore the relationship between MRI perfusion metrics (rCBV, rCBF), genetic markers, and contrast enhancement patterns in gliomas, aiming to enhance diagnostic accuracy and inform personalized therapeutic strategies. Additionally, other radiological features, such as the T2/FLAIR mismatch sign, are evaluated for their predictive utility in IDH mutations.

Study type

Retrospective cohort study.

Population

67 patients with brain tumors (including glioblastoma, astrocytoma, oligodendroglioma) undergoing surgical resection.

Field strength

1.5 Tesla MRI, including T1 pre- and post-contrast, FLAIR, DWI, and DSC sequences.

Assessment

Semiquantitative perfusion metrics (rCBV, rCBF) were evaluated against genetic markers (IDH1, EGFR, CDKN2A, PDGFRA, MGMT, TERT, 1p19q, PTEN, TP53, H3F3A) through advanced MRI techniques. Contrast enhancement was assessed, and genetic alterations were confirmed via histopathological and molecular analyses.

Statistical tests

Chi-square test, sensitivity, specificity, and ROC analysis for predictive modeling; significance level set at p < 0.05.

Results

Statistically significant differences in perfusion metrics were observed among tumors with distinct genetic profiles, with primary tumors and those harboring specific mutations (IDH1 wildtype, EGFR amplification, CDKN2A homozygous deletion, PDGFRA amplification) showing higher perfusion values. A cut-off value of <4 for rCBV in predicting IDH1 mutation yielded a sensitivity of 61.5 % and specificity of 82.1 %. For CDKN2A deletion, a cut-off of >5 resulted in a sensitivity of 75 % and specificity of 74.6 %, with an ROC value of 0.78.

Data conclusion

Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. Additionally, findings like the T2/FLAIR mismatch sign highlight the potential for preoperative molecular predictions when biopsy is not feasible. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.

Data conclusion

Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.