Abstract

Background

Previous intraindividual comparative studies evaluating gadobutrol and gadoteridol for contrast-enhanced magnetic resonance imaging (MRI) of brain tumours have relied on subjective image assessment, potentially leading to misleading conclusions. We used artificial intelligence algorithms to objectively compare the enhancement achieved with these contrast agents in glioblastoma patients.

Methods

Twenty-seven patients from a prior study who received identical doses of 0.1 mmol/kg gadobutrol and gadoteridol (with appropriate washout in between) were evaluated. Quantitative enhancement (QE) maps of the normalised enhancement of voxels, derived from computations based on the comparison of contrast-enhanced T1-weighted images relative to the harmonised intensity on unenhanced T1-weighted images, were compared. Bland-Altman analysis, linear regression analysis and Pearson correlation coefficient (r) determination were performed to compare net QE and per-region of interest (per-ROI) average QE (net QE divided by the number of voxels).

Results

No significant differences were observed for comparisons performed on net QE (mean difference -24.37 ± 620.8, p = 0.840, r = 0.989) or per-ROI average QE (0.0043 ± 0.0218, p = 0.313, r = 0.958). Bland-Altman analysis revealed better per-ROI average QE for gadoteridol-enhanced MRI in 19/27 (70.4%) patients although the mean difference (0.0043) was close to zero indicating high concordance and the absence of fixed bias.

Conclusions

The enhancement of glioblastoma achieved with gadoteridol and gadobutrol at 0.1 mmol/kg bodyweight is similar indicating that these agents have similar contrast efficacy and can be used interchangeably, confirming the results of a prior double-blind, randomised, intraindividual, crossover study.

Details

Title
The TRUTH confirmed: validation of an intraindividual comparison of gadobutrol and gadoteridol for imaging of glioblastoma using quantitative enhancement analysis
Author
Kuhn, Matthew J 1 ; Patriarche, Julia W 2 ; Patriarche, Douglas 2 ; Kirchin Miles A 3 ; Bona Massimo 3 ; Pirovano Gianpaolo 4 

 University of Illinois College of Medicine at Peoria, Peoria, USA (GRID:grid.430852.8) (ISNI:0000 0001 0741 4132); A.I. Analysis, Inc., Seattle, USA (GRID:grid.430852.8) 
 A.I. Analysis, Inc., Seattle, USA (GRID:grid.430852.8) 
 Bracco Imaging SpA, Global Medical & Regulatory Affairs, Milan, Italy (GRID:grid.476177.4) (ISNI:0000 0004 1755 9978) 
 Bracco Diagnostics, Inc., Global Medical & Regulatory Affairs, Monroe Township, USA (GRID:grid.418444.9) (ISNI:0000 0004 4904 7133) 
Publication year
2021
Publication date
Dec 2021
Publisher
Springer Nature B.V.
e-ISSN
25099280
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2580811686
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.