Abstract #1966
MRI-Based Radiomics Approach with Deep Learning for Distinguishing IDH-Mutant from IDH Wild-type Grade-4 Astrocytomas
Seyyed Ali Hosseini1,2, Elahe Hosseini3, Isaac Shiri4, Ghasem Hajianfar5, Stijn Servaes1,2, Pedro Rosa-Neto1,2, Laiz Godoy6, Stephen Bagley7, MacLean Nasrallah8, Donald M O’Rourke9, Suyash Mohan6, and SANJEEV CHAWLA6
1Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada, 2Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital,McGill University, Montreal, QC, Canada, 3Electrical and Computer Engineering, Kharazmi University, Tehran, Iran (Islamic Republic of), 4Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland, 5Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran (Islamic Republic of), 6Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, 7Hematology-Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, 8Clinical Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, 9Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
Synopsis
Keywords: Tumors, RadiomicsPatients (n=57) with isocitrate dehydrogenase (IDH) mutant grade-4 astrocytomas and IDH wild-type glioblastomas underwent anatomical imaging (post-contrast T1 and T2-FLAIR) on 3T magnet. Neoplasms were segmented into 5 ROIs and 105 radiomics features were extracted from each ROI. Features were subsequently selected using various algorithms. Patients were divided into two groups (50%-training and 50%-testing). A GAN-based deep learning algorithm was used to generate 1000 synthesized data-sets and four distinct deep-learning modules were implemented. The best model for differentiating two genotypes of neoplasms was obtained from core tumor regions by using K-best feature selection and Ensembled algorithm with high diagnostic performance.
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