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European Radiology
Published in: European Radiology 7/2017

01-07-2017 | Oncology

Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis

Authors: Yulia Lakhman, Harini Veeraraghavan, Joshua Chaim, Diana Feier, Debra A. Goldman, Chaya S. Moskowitz, Stephanie Nougaret, Ramon E. Sosa, Hebert Alberto Vargas, Robert A. Soslow, Nadeem R. Abu-Rustum, Hedvig Hricak, Evis Sala

Published in: European Radiology | Issue 7/2017

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Abstract

Purpose

To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA).

Methods

This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher’s exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM.

Results

Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, “T2 dark” area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79).

Conclusions

Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible.

Key Points

• Four qualitative MR features demonstrated the strongest statistical association with LMS.
• Combination of ≥3 these features could accurately differentiate LMS from ALM.
• Texture analysis was a feasible semi-automated approach for lesion categorization.
Appendix
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Metadata
Title
Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis
Authors
Yulia Lakhman
Harini Veeraraghavan
Joshua Chaim
Diana Feier
Debra A. Goldman
Chaya S. Moskowitz
Stephanie Nougaret
Ramon E. Sosa
Hebert Alberto Vargas
Robert A. Soslow
Nadeem R. Abu-Rustum
Hedvig Hricak
Evis Sala
Publication date
01-07-2017
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 7/2017
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-016-4623-9

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