Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps.

Autor: Tietze A; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany.; Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark., Nielsen A; Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark., Klærke Mikkelsen I; Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark., Bo Hansen M; Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark., Obel A; Dept. of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark., Østergaard L; Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark.; Dept. of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark., Mouridsen K; Center of Functionally Integrative Neuroscience, Clinical Institute, Aarhus University, Aarhus, Denmark.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2018 Sep 26; Vol. 13 (9), pp. e0202906. Date of Electronic Publication: 2018 Sep 26 (Print Publication: 2018).
DOI: 10.1371/journal.pone.0202906
Abstrakt: Purpose: The purpose of this work is to investigate if the curve-fitting algorithm in Dynamic Contrast Enhanced (DCE) MRI experiments influences the diagnostic quality of calculated parameter maps.
Material and Methods: We compared the Levenberg-Marquardt (LM) and a Bayesian method (BM) in DCE data of 42 glioma patients, using two compartmental models (extended Toft's and 2-compartment-exchange model). Logistic regression and an ordinal linear mixed model were used to investigate if the image quality differed between the curve-fitting algorithms and to quantify if image quality was affected for different parameters and algorithms. The diagnostic performance to discriminate between high-grade and low-grade gliomas was compared by applying a Wilcoxon signed-rank test (statistical significance p>0.05). Two neuroradiologists assessed different qualitative imaging features.
Results: Parameter maps based on BM, particularly those describing the blood-brain barrier, were superior those based on LM. The image quality was found to be significantly improved (p<0.001) for BM when assessed through independent clinical scores. In addition, given a set of clinical scores, the generating algorithm could be predicted with high accuracy (area under the receiver operating characteristic curve between 0.91 and 1). Using linear mixed models, image quality was found to be improved when applying the 2-compartment-exchange model compared to the extended Toft's model, regardless of the underlying fitting algorithm. Tumor grades were only differentiated reliably on plasma volume maps when applying BM. The curve-fitting algorithm had, however, no influence on grading when using parameter maps describing the blood-brain barrier.
Conclusion: The Bayesian method has the potential to increase the diagnostic reliability of Dynamic Contrast Enhanced parameter maps in brain tumors. In our data, images based on the 2-compartment-exchange model were superior to those based on the extended Toft's model.
Competing Interests: Three of the co-authors have competing interests by being a part of Cercare Medical. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Kim Mouridsen: COO Cercare Medical, https://cercare-medical.com/; Mikkel Bo Hansen: Chief Scientific Officer Cercare Medical, https://cercare-medical.com/; Anne Nielsen: Industrial PhD student Cercare Medical, https://cercare-medical.com/.
Databáze: MEDLINE
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