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Al-Mefty, Ossama

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Al-Mefty

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Ossama

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Al-Mefty, Ossama

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Now showing 1 - 3 of 3
  • Publication

    Fibrin Glue Injection for Cavernous Sinus Hemostasis Associated with Cranial Nerve Deficit: A Case Report

    (Georg Thieme Verlag KG, 2015) Tavanaiepour, Daryoush; Jernigan, Sarah; Abolfotoh, Mohamad; Al-Mefty, Ossama

    Fibrin glue injection has been used to control intraoperative cavernous sinus (CS) venous bleeding. There have been no reported complications related to this maneuver. We present a case where a patient developed a sensory trigeminal nerve deficit after injection of fibrin glue into the posterior CS during resection of a petrosal meningioma. We believe that this deficit was due to the compression of the trigeminal ganglion similar to balloon compression procedures. Although fibrin glue injection may achieve satisfactory cavernous sinus homeostasis, the volume and rate of injection should be kept in mind to avoid a compressive lesion on traversing cranial nerves and surrounding structures, or retrograde filling of the venous tributaries.

  • Publication

    Radiographic prediction of meningioma grade by semantic and radiomic features

    (Public Library of Science, 2017) Coroller, Thibaud; Bi, Wenya; Huynh, Elizabeth; Abedalthagafi, Malak; Aizer, Ayal A.; Greenwald, Noah; Parmar, Chintan; Narayan, Vivek; Wu, Winona; Miranda de Moura, Samuel; Gupta, Saksham; Beroukhim, Rameen; Wen, Patrick Y.; Al-Mefty, Ossama; Dunn, Ian; Santagata, Sandro; Alexander, Brian; Huang, Raymond; Aerts, Hugo

    Objectives: The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making. Methods: A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44). Results: Twelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62–0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84). Conclusions: We found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.

  • Publication

    Erratum: Genomic landscape of high-grade meningiomas

    (Nature Publishing Group UK, 2017) Bi, Wenya; Greenwald, Noah; Abedalthagafi, Malak; Wala, Jeremiah; Gibson, Will J.; Agarwalla, Pankaj Kumar; Horowitz, Peleg; Schumacher, Steven E.; Esaulova, Ekaterina; Mei, Yu; Chevalier, Aaron; A. Ducar, Matthew; Thorner, Aaron R.; van Hummelen, Paul; O. Stemmer-Rachamimov, Anat; Artyomov, Maksym; Al-Mefty, Ossama; Dunn, Gavin P.; Santagata, Sandro; Dunn, Ian; Beroukhim, Rameen