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Purpose <p>Despite advances in neuroimaging, reliable differentiation between meningiomas and dural metastases remains challenging because of overlapping imaging features. This study aimed to differentiate these entities using T2*-weighted DSC MRI metrics, particularly percentage signal recovery (PSR), integrated with conventional/contemporary MRI features and clinical variables.</p> Methods <p>This single-center retrospective study analyzed pretreatment MRI examinations between January 2020 and 2026 in patients with meningioma or dural metastasis. Two neuroradiologists evaluated imaging variables, including three DSC perfusion parameters (nCBV, nPSR<sub>lin</sub>, and nPSR<sub>log</sub>), ten conventional MRI features, two recently described MRI signs (outline sign and rim-enhancing pattern), and maximum tumor size. Clinical variables (age, sex, and history of cancer) and the primary origin of dural metastases were recorded. Univariable and multivariable logistic regression analyses were performed. Interobserver agreement was assessed using Cohen’s kappa or ICC.</p> Results <p>A total of 70 meningiomas from 59 patients and 54 dural metastases from 38 patients were included. There were no significant differences in age or sex (<i>p</i> = 0.10 and <i>p</i> = 1.00). There was no significant difference in the presence of the outline sign or rim-enhancing pattern (<i>p</i> = 0.674 and <i>p</i> = 0.111). History of malignancy was more frequent in dural metastasis (86.8% vs. 27.1%, <i>p</i> &lt; 0.001). Multivariable analysis revealed four independent predictors: history of cancer, nPSR<sub>log</sub>, peritumoral edema, and intratumoral calcification. The final model achieved an AUC, accuracy, sensitivity, specificity, and Brier score of 0.98, 96.0%, 98.1%, 94.3%, and 0.047.</p> Conclusion <p>Three MRI-derived features combined with the history of cancer demonstrated strong predictive performance and may aid diagnostic decision-making in challenging cases.</p>

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Meningioma versus dural metastasis: integration of T2*-DSC perfusion–derived percentage signal recovery with conventional and contemporary MRI features and clinical variables

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Sabahattin Yuzkan,
  • Elif Oyku Yesiloglu,
  • Irem Sena Konakci,
  • Huseyin Ekin Ergin,
  • Oyku Puskulluoglu,
  • Ceren Karabiber Deveci,
  • Yunus Emre Senturk,
  • Ibrahim Kulac

摘要

Purpose

Despite advances in neuroimaging, reliable differentiation between meningiomas and dural metastases remains challenging because of overlapping imaging features. This study aimed to differentiate these entities using T2*-weighted DSC MRI metrics, particularly percentage signal recovery (PSR), integrated with conventional/contemporary MRI features and clinical variables.

Methods

This single-center retrospective study analyzed pretreatment MRI examinations between January 2020 and 2026 in patients with meningioma or dural metastasis. Two neuroradiologists evaluated imaging variables, including three DSC perfusion parameters (nCBV, nPSRlin, and nPSRlog), ten conventional MRI features, two recently described MRI signs (outline sign and rim-enhancing pattern), and maximum tumor size. Clinical variables (age, sex, and history of cancer) and the primary origin of dural metastases were recorded. Univariable and multivariable logistic regression analyses were performed. Interobserver agreement was assessed using Cohen’s kappa or ICC.

Results

A total of 70 meningiomas from 59 patients and 54 dural metastases from 38 patients were included. There were no significant differences in age or sex (p = 0.10 and p = 1.00). There was no significant difference in the presence of the outline sign or rim-enhancing pattern (p = 0.674 and p = 0.111). History of malignancy was more frequent in dural metastasis (86.8% vs. 27.1%, p < 0.001). Multivariable analysis revealed four independent predictors: history of cancer, nPSRlog, peritumoral edema, and intratumoral calcification. The final model achieved an AUC, accuracy, sensitivity, specificity, and Brier score of 0.98, 96.0%, 98.1%, 94.3%, and 0.047.

Conclusion

Three MRI-derived features combined with the history of cancer demonstrated strong predictive performance and may aid diagnostic decision-making in challenging cases.