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Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue
OBJECTIVE: ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms’ tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018931/ https://www.ncbi.nlm.nih.gov/pubmed/32617696 http://dx.doi.org/10.1007/s10334-020-00862-4 |
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author | Rogers, Harriet J. Verhagen, Martijn V. Clark, Chris A. Hales, Patrick W. |
author_facet | Rogers, Harriet J. Verhagen, Martijn V. Clark, Chris A. Hales, Patrick W. |
author_sort | Rogers, Harriet J. |
collection | PubMed |
description | OBJECTIVE: ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms’ tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion models (mono exponential, IVIM [Intravoxel Incoherent Motion], stretched exponential, and kurtosis) in Wilms’ tumours and the unaffected contralateral kidneys. MATERIALS AND METHODS: DWI data were retrospectively reviewed (110 Wilms’ tumours and 75 normal kidney datasets). The goodness of fit for each model was measured voxel-wise using Akaike Information Criteria (AIC). Mean AIC was calculated for each tumour volume (or contralateral normal kidney tissue). One-way ANOVAs with Greenhouse–Geisser correction and post hoc tests using the Bonferroni correction evaluated significant differences between AIC values; the lowest AIC indicating the optimum model. RESULTS: IVIM and stretched exponential provided the best fits to the Wilms’ tumour DWI data. IVIM provided the best fit for the normal kidney data. Mono exponential was the least appropriate fitting method for both Wilms’ tumour and normal kidney data. DISCUSSION: The diffusion weighted signal in Wilms’ tumours and normal kidney tissue does not exhibit a mono-exponential decay and is better described by non-Gaussian models of diffusion. |
format | Online Article Text |
id | pubmed-8018931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-80189312021-04-16 Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue Rogers, Harriet J. Verhagen, Martijn V. Clark, Chris A. Hales, Patrick W. MAGMA Research Article OBJECTIVE: ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms’ tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion models (mono exponential, IVIM [Intravoxel Incoherent Motion], stretched exponential, and kurtosis) in Wilms’ tumours and the unaffected contralateral kidneys. MATERIALS AND METHODS: DWI data were retrospectively reviewed (110 Wilms’ tumours and 75 normal kidney datasets). The goodness of fit for each model was measured voxel-wise using Akaike Information Criteria (AIC). Mean AIC was calculated for each tumour volume (or contralateral normal kidney tissue). One-way ANOVAs with Greenhouse–Geisser correction and post hoc tests using the Bonferroni correction evaluated significant differences between AIC values; the lowest AIC indicating the optimum model. RESULTS: IVIM and stretched exponential provided the best fits to the Wilms’ tumour DWI data. IVIM provided the best fit for the normal kidney data. Mono exponential was the least appropriate fitting method for both Wilms’ tumour and normal kidney data. DISCUSSION: The diffusion weighted signal in Wilms’ tumours and normal kidney tissue does not exhibit a mono-exponential decay and is better described by non-Gaussian models of diffusion. Springer International Publishing 2020-07-02 2021 /pmc/articles/PMC8018931/ /pubmed/32617696 http://dx.doi.org/10.1007/s10334-020-00862-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Article Rogers, Harriet J. Verhagen, Martijn V. Clark, Chris A. Hales, Patrick W. Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue |
title | Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue |
title_full | Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue |
title_fullStr | Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue |
title_full_unstemmed | Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue |
title_short | Comparison of models of diffusion in Wilms’ tumours and normal contralateral renal tissue |
title_sort | comparison of models of diffusion in wilms’ tumours and normal contralateral renal tissue |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018931/ https://www.ncbi.nlm.nih.gov/pubmed/32617696 http://dx.doi.org/10.1007/s10334-020-00862-4 |
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