Can Dynamic Contrast-enhanced MRI Contribute to Improved Assessment of Rectosigmoid Involvement in Deep Infiltrating Endometriosis?

Endometriosis compressed-sensing deep infiltrating endometriosis dynamic contrast-enhanced MRI mushroom cap sign

Journal

In vivo (Athens, Greece)
ISSN: 1791-7549
Titre abrégé: In Vivo
Pays: Greece
ID NLM: 8806809

Informations de publication

Date de publication:
Historique:
received: 19 04 2021
revised: 05 05 2021
accepted: 06 05 2021
entrez: 28 6 2021
pubmed: 29 6 2021
medline: 1 7 2021
Statut: ppublish

Résumé

To determine whether a prototypical compressed-sensing volume-interpolated breath-hold (csVIBE) provides diagnostic value in detecting rectosigmoid infiltration in deep infiltrating endometriosis (DIE). csVIBE was employed in 151 women undergoing pelvic magnetic resonance imaging, of whom 43 had undergone surgery for suspected endometriosis. The accuracy of T2-weighted BLADE and BLADE/csVIBE, additional diagnostic value of csVIBE, and diagnostic confidence were rated by two readers. Additionally, the presence of the "mushroom cap sign" was assessed on BLADE and csVIBE. The diagnostic accuracy, sensitivity, and specificity of BLADE and BLADE/csVIBE were not significantly different between Readers A and B. For both readers, the confidence in the diagnosis increased with csVIBE, but this increase in the odds ratio was not significant for both readers. Both readers preferred csVIBE over BLADE with regard to detection of the "mushroom cap sign." csVIBE may provide a diagnostic benefit for surgical strategy selection through better delineation of the "mushroom cap sign."

Sections du résumé

BACKGROUND/AIM OBJECTIVE
To determine whether a prototypical compressed-sensing volume-interpolated breath-hold (csVIBE) provides diagnostic value in detecting rectosigmoid infiltration in deep infiltrating endometriosis (DIE).
PATIENTS AND METHODS METHODS
csVIBE was employed in 151 women undergoing pelvic magnetic resonance imaging, of whom 43 had undergone surgery for suspected endometriosis. The accuracy of T2-weighted BLADE and BLADE/csVIBE, additional diagnostic value of csVIBE, and diagnostic confidence were rated by two readers. Additionally, the presence of the "mushroom cap sign" was assessed on BLADE and csVIBE.
RESULTS RESULTS
The diagnostic accuracy, sensitivity, and specificity of BLADE and BLADE/csVIBE were not significantly different between Readers A and B. For both readers, the confidence in the diagnosis increased with csVIBE, but this increase in the odds ratio was not significant for both readers. Both readers preferred csVIBE over BLADE with regard to detection of the "mushroom cap sign."
CONCLUSION CONCLUSIONS
csVIBE may provide a diagnostic benefit for surgical strategy selection through better delineation of the "mushroom cap sign."

Identifiants

pubmed: 34182500
pii: 35/4/2217
doi: 10.21873/invivo.12494
pmc: PMC8286483
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2217-2226

Informations de copyright

Copyright © 2021 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

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Auteurs

Daniel Hausmann (D)

Department of Radiology, Kantonsspital Baden, Baden, Switzerland; daniel.hausmann@ksb.ch.
Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.

Vasiliki Perignon (V)

Department of Radiology, Kantonsspital Baden, Baden, Switzerland.

Regula Grabherr (R)

Department of Gynecology, Kantonsspital Baden, Baden, Switzerland.

Elisabeth Weiland (E)

MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.

Marcel Dominik Nickel (MD)

MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.

Maurus Murer (M)

Department of Pathology, Kantonsspital Baden, Baden, Switzerland.

Lars Bosshard (L)

Nexus Personalized Health Technologies, ETH Zurich, and Swiss Institute for Bioinformatics (SIB), Zurich, Switzerland.

Michael Prummer (M)

Nexus Personalized Health Technologies, ETH Zurich, and Swiss Institute for Bioinformatics (SIB), Zurich, Switzerland.

Rahel A Kubik-Huch (RA)

Department of Radiology, Kantonsspital Baden, Baden, Switzerland.

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