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Abstract #4309

Susceptibility Weighted MRI for Predicting Critical Developmental Regulatory S100 Proteins in Meningiomas at 3T

Sena Azamat1,2, Buse Buz-Yaluğ1, Abdullah Baş1, Alpay Ozcan3, Ayça Ersen Danyeli4,5, Kubra Tan6, Ozge Can7, Necmettin Pamir5,8, Alp Dinçer5,9, Koray Ozduman5,8, and Esin Ozturk-Isik1
1Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, 2Department of Radiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey, 3Electric and Electronic Engineering Department, Bogazici University, Istanbul, Turkey, 4Department of Medical Pathology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 5Center for Neuroradiological Applications and Reseach, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 6Health Institutes of Turkey, Istanbul, Turkey, 7Department of Medical Engineering, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 8Department of Neurosurgery, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 9Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey

Synopsis

Meningiomas are the most common primary intracranial tumors in adults. S100 protein expression (S100+) in meningiomas is a marker of neural crest origin. Eighty-four patients with preoperative MRI were included in this IRB approved study. The whole tumor volumes were segmented from FLAIR, followed by co-registration onto SWI. Supervised machine and deep learning methods were employed to categorize meningiomas into S100+ and S100- groups based on SWI histogram values. Ensemble bagged trees resulted in an accuracy of 85.7% (sensitivity=87.0 % and specificity=84.4 %), while a Resnet 50 architecture had 70.5% accuracy (sensitivity=80%, specificity=57.1%) for predicting S100 protein expression in meningiomas.

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Keywords