Cargando…

Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas

BACKGROUND: Dynamic-susceptibility contrast and diffusion-weighted imaging are promising techniques in diagnosing glioma grade. PURPOSE: To compare the inter-observer reproducibility of multiple dynamic-susceptibility contrast and diffusion-weighted imaging parameters and to assess their potential i...

Descripción completa

Detalles Bibliográficos
Autores principales: Dijkstra, Hildebrand, Sijens, Paul E, van der Hoorn, Anouk, van Laar, Peter Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6935831/
https://www.ncbi.nlm.nih.gov/pubmed/31159557
http://dx.doi.org/10.1177/0284185119852729
_version_ 1783483642418298880
author Dijkstra, Hildebrand
Sijens, Paul E
van der Hoorn, Anouk
van Laar, Peter Jan
author_facet Dijkstra, Hildebrand
Sijens, Paul E
van der Hoorn, Anouk
van Laar, Peter Jan
author_sort Dijkstra, Hildebrand
collection PubMed
description BACKGROUND: Dynamic-susceptibility contrast and diffusion-weighted imaging are promising techniques in diagnosing glioma grade. PURPOSE: To compare the inter-observer reproducibility of multiple dynamic-susceptibility contrast and diffusion-weighted imaging parameters and to assess their potential in differentiating low- and high-grade gliomas. MATERIAL AND METHODS: Thirty patients (16 men; mean age = 40.6 years) with low-grade (n = 13) and high-grade (n = 17) gliomas and known pathology, scanned with dynamic-susceptibility contrast and diffusion-weighted imaging were included retrospectively between March 2006 and March 2014. Three observers used three different methods to define the regions of interest: (i) circles at maximum perfusion and minimum apparent diffusion coefficient; (ii) freeform 2D encompassing the tumor at largest cross-section only; (iii) freeform 3D on all cross-sections. The dynamic-susceptibility contrast curve was analyzed voxelwise: maximum contrast enhancement; time-to-peak; wash-in rate; wash-out rate; and relative cerebral blood volume. The mean was calculated for all regions of interest. For 2D and 3D methods, histogram analysis yielded additional statistics: the minimum and maximum 5% and 10% pixel values of the tumor (min5%, min10%, max5%, max10%). Intraclass correlations coefficients (ICC) were calculated between observers. Low- and high-grade tumors were compared with independent t-tests or Mann–Whitney tests. RESULTS: ICCs were highest for 3D freeform (ICC = 0.836–0.986) followed by 2D freeform (ICC = 0.854–0.974) and circular regions of interest (0.141–0.641). High ICC and significant discrimination between low- and high-grade gliomas was found for the following optimized parameters: apparent diffusion coefficient (P < 0.001; ICC = 0.641; mean; circle); time-to-peak (P = 0.015; ICC = 0.986; mean; 3D); wash-in rate (P = 0.004; ICC = 0.826; min10%; 3D); wash-out rate (P < 0.001; ICC = 0.860; min10%; 2D); and relative cerebral blood volume (P ≤ 0.001; ICC = 0.961; mean; 3D). CONCLUSION: Dynamic-susceptibility contrast perfusion parameters relative cerebral blood volume and time-to-peak yielded high inter-observer reproducibility and significant glioma grade differentiation for the means of 2D and 3D freeform regions of interest. Choosing a freeform 2D method optimizes observer agreement and differentiation in clinical practice, while a freeform 3D method provides no additional benefit.
format Online
Article
Text
id pubmed-6935831
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-69358312020-02-07 Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas Dijkstra, Hildebrand Sijens, Paul E van der Hoorn, Anouk van Laar, Peter Jan Acta Radiol Neuro Imaging BACKGROUND: Dynamic-susceptibility contrast and diffusion-weighted imaging are promising techniques in diagnosing glioma grade. PURPOSE: To compare the inter-observer reproducibility of multiple dynamic-susceptibility contrast and diffusion-weighted imaging parameters and to assess their potential in differentiating low- and high-grade gliomas. MATERIAL AND METHODS: Thirty patients (16 men; mean age = 40.6 years) with low-grade (n = 13) and high-grade (n = 17) gliomas and known pathology, scanned with dynamic-susceptibility contrast and diffusion-weighted imaging were included retrospectively between March 2006 and March 2014. Three observers used three different methods to define the regions of interest: (i) circles at maximum perfusion and minimum apparent diffusion coefficient; (ii) freeform 2D encompassing the tumor at largest cross-section only; (iii) freeform 3D on all cross-sections. The dynamic-susceptibility contrast curve was analyzed voxelwise: maximum contrast enhancement; time-to-peak; wash-in rate; wash-out rate; and relative cerebral blood volume. The mean was calculated for all regions of interest. For 2D and 3D methods, histogram analysis yielded additional statistics: the minimum and maximum 5% and 10% pixel values of the tumor (min5%, min10%, max5%, max10%). Intraclass correlations coefficients (ICC) were calculated between observers. Low- and high-grade tumors were compared with independent t-tests or Mann–Whitney tests. RESULTS: ICCs were highest for 3D freeform (ICC = 0.836–0.986) followed by 2D freeform (ICC = 0.854–0.974) and circular regions of interest (0.141–0.641). High ICC and significant discrimination between low- and high-grade gliomas was found for the following optimized parameters: apparent diffusion coefficient (P < 0.001; ICC = 0.641; mean; circle); time-to-peak (P = 0.015; ICC = 0.986; mean; 3D); wash-in rate (P = 0.004; ICC = 0.826; min10%; 3D); wash-out rate (P < 0.001; ICC = 0.860; min10%; 2D); and relative cerebral blood volume (P ≤ 0.001; ICC = 0.961; mean; 3D). CONCLUSION: Dynamic-susceptibility contrast perfusion parameters relative cerebral blood volume and time-to-peak yielded high inter-observer reproducibility and significant glioma grade differentiation for the means of 2D and 3D freeform regions of interest. Choosing a freeform 2D method optimizes observer agreement and differentiation in clinical practice, while a freeform 3D method provides no additional benefit. SAGE Publications 2019-06-03 2020-01 /pmc/articles/PMC6935831/ /pubmed/31159557 http://dx.doi.org/10.1177/0284185119852729 Text en © The Foundation Acta Radiologica 2019 http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Neuro Imaging
Dijkstra, Hildebrand
Sijens, Paul E
van der Hoorn, Anouk
van Laar, Peter Jan
Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas
title Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas
title_full Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas
title_fullStr Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas
title_full_unstemmed Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas
title_short Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas
title_sort inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion mri parameters in histogram analysis of gliomas
topic Neuro Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6935831/
https://www.ncbi.nlm.nih.gov/pubmed/31159557
http://dx.doi.org/10.1177/0284185119852729
work_keys_str_mv AT dijkstrahildebrand interobserverreproducibilityofquantitativedynamicsusceptibilitycontrastanddiffusionmriparametersinhistogramanalysisofgliomas
AT sijenspaule interobserverreproducibilityofquantitativedynamicsusceptibilitycontrastanddiffusionmriparametersinhistogramanalysisofgliomas
AT vanderhoornanouk interobserverreproducibilityofquantitativedynamicsusceptibilitycontrastanddiffusionmriparametersinhistogramanalysisofgliomas
AT vanlaarpeterjan interobserverreproducibilityofquantitativedynamicsusceptibilitycontrastanddiffusionmriparametersinhistogramanalysisofgliomas