Abstract #2837
Identification of S100 Immunopositivity on T2-weighted MRI Using Deep Learning
Abdullah Baş1, Kübra Tan2, Ayça Ersen Danyeli3,4, M.Necmettin Pamir5,6, Alp Dincer4,7, Koray Ozduman5,6, Ozge Can8, and Esin Ozturk-Isik1
1Institute of Biomedical Engineering, Bogazici University, İstanbul, Turkey, 2Health Institutes of Turkey, Istanbul, Turkey, 3Department of Medical Pathology, Acibadem Mehmet Ali Aydinlar University, İstanbul, Turkey, 4Center for Neuroradiological Applications and Reseach, Acibadem Mehmet Ali Aydinlar University, İstanbul, Turkey, 5Center for Neuroradiological Applications and Reseach, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 6Department of Neurosurgery, Acibadem Mehmet Ali Aydinlar University, İstanbul, Turkey, 7Department of Radiology, Acibadem Mehmet Ali Aydinlar University, İstanbul, Turkey, 8Center for Neuroradiological Applications and Reseach Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
Synopsis
S100 protein expression is a relevant indicator of prognosis in meningiomas and it is more common in benign meningiomas. To our knowledge, a clinically feasible non-invasive method that preoperatively identifies S100 protein expression is not available. In this study, we proposed registration-free deep learning models to predict S100 expression non-invasively using T2-w MRI. The proposed hybrid deep learning model could predict S100 protein expression in meningiomas using T2-w MRI, with 91% accuracy on the validation set, and 83% accuracy on the test set.
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