Abstract #4741

Distinction of True-Progression from Pseudo-progression in Glioblastomas using ML Model based on Quantitative mpMRI and Molecular Signatures

Virendra Kumar Yadav¹, Suyash Mohan², Sumeet Kumar Agarwal^3,4, Laiz Laura de Godoy², Sumei Wang², MacLean P. Nasrallah⁵, Donald M. O’Rourke⁶, Stephen Bagley⁷, Harish Poptani⁸, Sanjeev Chawla², and Anup Kumar Singh^1,4,9

¹Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, India, ²Departments of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ³Department of Electrical Engineering, Indian Institute of Technology, Delhi, India, ⁴Yardi School of Artificial Intelligence, Indian Institute of Technology, Delhi, India, ⁵Clinical Pathology and Laboratory, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ⁶Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ⁷Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ⁸Department of Molecular and Clinical Cancer Medicine, University of Liverpool, United Kingdom, Liverpool, United Kingdom, ⁹Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

Synopsis

Keywords: Machine Learning/Artificial Intelligence, BrainGlioblastoma patients (n=93) exhibiting enhancing lesions within 6 months after completion of standard therapy underwent anatomical imaging, diffusion and perfusion MRI. The median values of parameters (MD, FA, CL, CP, CS and rCBV) were computed from the enhancing regions. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status was available from 75 patients. Subsequently, these patients were classified as TP (n=55) or PsP (n=20). The data were randomly split into training and testing sets. The best model for differentiating TP from PsP was obtained using quadratic SVM classifier with a training accuracy of 90.9%, cross-validation accuracy of 85.5% and testing accuracy of 85%.

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