Meeting Banner
Abstract #2749

Machine Learning based Differentiation of Non-Enhancing Tumor from Vasogenic Edema in patients with Low Grade Gliomas using DCE-MRI

Virendra Kumar Yadav1, Rakesh Kumar Gupta2, Sumeet Agarwal3, and Anup Singh1,4
1Center for Biomedical Engineering, Indian Institute of Technology, Delhi, India, 2Fortis Memorial Research Institute, Gurugram, India, 3Electrical Engineering, Indian Institute of Technology, Delhi, India, 4Biomedical Engineering, All India Institute of Medical Sciences, Delhi, India


Precise safe surgical resection and precisely directed radiation are important in clinical practice for low-grade gliomas (LGGs) patients in order to minimize the neurological deficit and radiation toxicity, respectively. Clinicians find difficulty in defining the border between the non-enhancing tumor and edema components. In this study, machine learning-based models were developed in order to distinguish non-enhancing tumors from vasogenic edema using quantitative perfusion parameters obtained using dynamic-contrast-enhanced MRI. The proposed approach may help in assisting radiologists, by defining precise tumor boundaries and hence, results in improving patients’ quality of life and overall survival.

This abstract and the presentation materials are available to members only; a login is required.

Join Here