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Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression

Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the...

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Autores principales: Metz, Marie-Christin, Molina-Romero, Miguel, Lipkova, Jana, Gempt, Jens, Liesche-Starnecker, Friederike, Eichinger, Paul, Grundl, Lioba, Menze, Bjoern, Combs, Stephanie E., Zimmer, Claus, Wiestler, Benedikt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140058/
https://www.ncbi.nlm.nih.gov/pubmed/32204544
http://dx.doi.org/10.3390/cancers12030728
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author Metz, Marie-Christin
Molina-Romero, Miguel
Lipkova, Jana
Gempt, Jens
Liesche-Starnecker, Friederike
Eichinger, Paul
Grundl, Lioba
Menze, Bjoern
Combs, Stephanie E.
Zimmer, Claus
Wiestler, Benedikt
author_facet Metz, Marie-Christin
Molina-Romero, Miguel
Lipkova, Jana
Gempt, Jens
Liesche-Starnecker, Friederike
Eichinger, Paul
Grundl, Lioba
Menze, Bjoern
Combs, Stephanie E.
Zimmer, Claus
Wiestler, Benedikt
author_sort Metz, Marie-Christin
collection PubMed
description Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC–DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC–FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC–FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions.
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spelling pubmed-71400582020-04-13 Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression Metz, Marie-Christin Molina-Romero, Miguel Lipkova, Jana Gempt, Jens Liesche-Starnecker, Friederike Eichinger, Paul Grundl, Lioba Menze, Bjoern Combs, Stephanie E. Zimmer, Claus Wiestler, Benedikt Cancers (Basel) Article Diffusion tensor imaging (DTI), and fractional-anisotropy (FA) maps in particular, have shown promise in predicting areas of tumor recurrence in glioblastoma. However, analysis of peritumoral edema, where most recurrences occur, is impeded by free-water contamination. In this study, we evaluated the benefits of a novel, deep-learning-based approach for the free-water correction (FWC) of DTI data for prediction of later recurrence. We investigated 35 glioblastoma cases from our prospective glioma cohort. A preoperative MR image and the first MR scan showing tumor recurrence were semiautomatically segmented into areas of contrast-enhancing tumor, edema, or recurrence of the tumor. The 10th, 50th and 90th percentiles and mean of FA and mean-diffusivity (MD) values (both for the original and FWC–DTI data) were collected for areas with and without recurrence in the peritumoral edema. We found significant differences in the FWC–FA maps between areas of recurrence-free edema and areas with later tumor recurrence, where differences in noncorrected FA maps were less pronounced. Consequently, a generalized mixed-effect model had a significantly higher area under the curve when using FWC–FA maps (AUC = 0.9) compared to noncorrected maps (AUC = 0.77, p < 0.001). This may reflect tumor infiltration that is not visible in conventional imaging, and may therefore reveal important information for personalized treatment decisions. MDPI 2020-03-19 /pmc/articles/PMC7140058/ /pubmed/32204544 http://dx.doi.org/10.3390/cancers12030728 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Metz, Marie-Christin
Molina-Romero, Miguel
Lipkova, Jana
Gempt, Jens
Liesche-Starnecker, Friederike
Eichinger, Paul
Grundl, Lioba
Menze, Bjoern
Combs, Stephanie E.
Zimmer, Claus
Wiestler, Benedikt
Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression
title Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression
title_full Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression
title_fullStr Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression
title_full_unstemmed Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression
title_short Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression
title_sort predicting glioblastoma recurrence from preoperative mr scans using fractional-anisotropy maps with free-water suppression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140058/
https://www.ncbi.nlm.nih.gov/pubmed/32204544
http://dx.doi.org/10.3390/cancers12030728
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