A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change

Felix Eisenhut; Tobias Engelhorn; Soheil Arinrad; Sebastian Brandner; Roland Coras; Florian Putz; Rainer Fietkau; Arnd Doerfler; Manuel A Schmidt

doi:10.3390/diagnostics11122281

A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change

Diagnostics (Basel). 2021 Dec 6;11(12):2281. doi: 10.3390/diagnostics11122281.

Authors

Felix Eisenhut¹, Tobias Engelhorn¹, Soheil Arinrad², Sebastian Brandner², Roland Coras³, Florian Putz⁴, Rainer Fietkau⁴, Arnd Doerfler¹, Manuel A Schmidt¹

Affiliations

¹ Department of Neuroradiology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany.
² Department of Neurosurgery, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany.
³ Department of Neuropathology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany.
⁴ Department of Radiation Oncology, University Hospital Erlangen, Universitaetsstrasse 27, 91054 Erlangen, Germany.

Abstract

To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, mean, and maximum ratios_CBV (CBV_lesion to CBV_{healthy white matter}) were computed. All data were tested for lesion discrimination. A multiparametric model was compiled via multiple logistic regression using data demonstrating significant difference between GBM and TRC and tested for its diagnostic strength in an independent patient cohort. A total of 34 patients (17 patients with recurrent GBM and 17 patients with TRC) were included. ADC measurements showed no significant difference between both entities. All CBV and ratios_CBV measurements were significantly higher in patients with recurrent GBM than TRC. A minimum CBV of 8.5, mean CBV of 116.5, maximum CBV of 327 and ratio_CBV _minimum of 0.17, ratio_CBV _mean of 2.26 and ratio_CBV _maximum of 3.82 were computed as optimal cut-off values. By integrating these parameters in a multiparametric model and testing it in an independent patient cohort, 9 of 10 patients, i.e., 90%, were classified correctly. The multiparametric model further improves radiological discrimination of GBM from TRC in comparison to single-parameter approaches and enables reliable identification of recurrent tumors.

Keywords: apparent diffusion coefficient; cerebral radiation necrosis; dynamic susceptibility contrast perfusion imaging; glioblastoma; multivariate logistic regression; pseudoprogression; treatment-related changes.