Meeting Banner
Abstract #1847

Logistic regression of multiparametric MR for glioma grading

Lawrence Kenning 1 , Martin Lowry 2 , Martin Pickles 2 , Christopher Roland-Hill 3 , Shailendra Achawal 3 , Chittoor Rajaraman 3 , and Lindsay W Turnbull 2,3

1 Centre for Magnetic Resonance Investigations, University of Hull, Hull, East Riding of Yorkshire, United Kingdom, 2 Centre for Magnetic Resonance Investigations, Hull York Medical School at the University of Hull, Hull, East Riding of Yorkshire, United Kingdom, 3 Hull and East Yorkshire Hospitals NHS Trust, East Riding of Yorkshire, United Kingdom

We investigated the role of functional MR parameters to determine glioma grade using logistic regression models. DTI, DCE and DSC were acquired from 55 glioma patients and ADC, FA, q, RA, λL, λR, R1, Ktrans, ve, vb, rCBVGVF, rCBVBOX, and K2 were calculated. Tumour volumes of interest to sample the data were contoured using morphological imaging. Using multi-parametric MR, 82.8% of cases were correctly classified using a two part logistic regression model decision tree. DTI and DCE appeared to be the most useful for determining lesion grade in this cohort of patients.

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

Join Here