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PREOPERATIVE MAGNETIC RESONANCE IMAGING DIAGNOSTIC FEATURES OF UTERINE LEIOMYOSARCOMAS: A SYSTEMATIC REVIEW

Published online by Cambridge University Press:  12 April 2018

Helen Kaganov ,
Alex Ades  and
David Stuart Fraser
Helen Kaganov
Affiliation:
Department of Obstetrics and Gynaecology, Box Hill Hospitalhelen.kaganov@gmail.com
Alex Ades
Affiliation:
Department of Obstetrics and Gynaecology, The Royal Women's Hospital
David Stuart Fraser
Affiliation:
Faculty of Science and Engineering
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Abstract

Objectives: There are no current established pathognomonic diagnostic features for uterine leiomyosarcomas in the pre- or perioperative setting. Recent inadvertent upstaging of this rare malignancy during laparoscopic morcellation of a presumed fibroid has prompted widespread debate among clinicians regarding the safety of current surgical techniques for management of fibroids. This study aims to conduct a systematic review investigating significant diagnostic features in magnetic resonance imaging (MRI) of uterine leiomyosarcomas.

Methods: A comprehensive database search was conducted guided by PRISMA recommendations for peer-reviewed publications to November 2017. Parameters available in MRI were compared for reliability and accuracy of diagnosis of leiomyosarcomas. A decision tree algorithm classifier model was constructed to investigate whether T1 and T2 MRI signal intensities are useful indicators.

Results: Nine eligible studies were identified for analysis. There appears to be a significant relationship between histopathological type and T1 and T2 intensity signals (p < .05). A decision tree model analyzing T1 and T2 signal intensity readings supports this trend, with a diagnostic specificity of 77.78 percent for uterine leiomyosarcomas. The apparent diffusion coefficient (ADC) values were not observed to have a significant relationship with tumor pathology (p = .18).

Conclusions: Various studies have investigated pre- and perioperative techniques in differentiating uterine leiomyosarcoma from benign fibroids. Given the rarity of the malignancy and lack of pathognomonic diagnostic parameters, there is difficulty in establishing definitive criteria. A decision tree model is proposed to aid diagnosis based on MRI signal intensities.

Keywords

FibroidsImagingLeiomyosarcomaMorcellationMRI
Type
Assessment
Information
International Journal of Technology Assessment in Health Care , Volume 34 , Issue 2 , 2018 , pp. 172 - 179
Copyright
Copyright © Cambridge University Press 2018 

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