ArticleNeuroradiology2025
MRI evaluation of hyperostosis frontalis interna: differentiation and diagnostic insights.
Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionTo delineate the MRI characteristics of Hyperostosis Frontalis Interna (HFI) and evaluate its imaging features to aid in accurate diagnosis and differentiation from other pathologies, particularly metastatic disease.
methodsA retrospective analysis of 74 patients with HFI was conducted. MRI sequences, including pre-contrast T1-weighted, post-contrast T1-weighted, and T2-weighted fat-saturation imaging, were evaluated. Quantitative and qualitative assessments of HFI signal intensity and enhancement patterns were performed. The Hershkovitz classification categorized the extent of HFI.
resultsPre-contrast T1-weighted imaging showed varied signal intensities, with hypointense regions more common in advanced HFI stages. Hypointense HFI was associated with a lower likelihood (OR: 0.303, CI: 0.113-0.808) and heterogeneous distribution was associated with a higher likelihood (OR: 5.128, CI: 1.982-13.265) of higher Hershkovitz classification (P = 0.0008). Post-contrast T1-weighted imaging revealed that 31% of subjects demonstrated enhancement, with focal geographic enhancement associated with lower CT attenuation values (P = 0.0138), indicating higher fat content. T2-weighted fat-saturation imaging supported the correlation between hypointense signals on pre-contrast T1 imaging and lower T2 signal intensities (P = 0.0022). No significant differences were found in enhancement patterns between different MRI sequences (P > 0.1326).
conclusionsHFI demonstrates varying appearances on pre- and post-contrast MRI sequences, crucial for differentiating benign HFI from metastatic lesions. Understanding these imaging characteristics can enhance diagnostic accuracy, reduce the risk of misdiagnosis, and improve patient management. Future studies should focus on larger, more diverse populations and explore advanced MRI techniques to further understand HFI.
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