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European Radiology

, Volume 27, Issue 7, pp 2903–2915 | Cite as

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

  • 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
Oncology

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.

Keywords

Magnetic Resonance Imaging Uterine Leiomyosarcoma Uterine Leiomyoma Atypical Uterine Leiomyoma Texture Analysis 

Abbreviations

ALM

Atypical leiomyoma

BW

Bandwidth

CI

Confidence interval

FOV

Field of view

GLCM

Gray-level co-occurrence matrices

HIPAA

Health Insurance Portability and Accountability Act

LM

Uterine leiomyomas

LMS

Leiomyosarcoma

MR

Magnetic resonance

MRI

Magnetic resonance imaging

NPV

Negative predictive value

PPV

Positive predictive value

ROIs

Regions of interest

SI

Signal-intensity

ST

Section thickness

T2WI

T2-weighted image

TA

Texture analysis

DWI

Diffusion-weighted imaging

Notes

Acknowledgments

The scientific guarantor of this publication is Yulia Lakhman MD. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748. Two of the authors have significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board.

No study subjects or cohorts have been previously reported. Methodology: retrospective, cross-sectional study, performed at one institution.

Authors thank Ada Muellner M.S. and Joanne Chin M.F.A. for their editorial assistance with the manuscript.

Supplementary material

330_2016_4623_MOESM1_ESM.docx (19 kb)
ESM 1 (DOCX 19 kb)

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Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Yulia Lakhman
    • 1
  • Harini Veeraraghavan
    • 2
  • Joshua Chaim
    • 1
  • Diana Feier
    • 1
    • 3
  • Debra A. Goldman
    • 4
  • Chaya S. Moskowitz
    • 4
  • Stephanie Nougaret
    • 1
    • 5
  • Ramon E. Sosa
    • 1
  • Hebert Alberto Vargas
    • 1
  • Robert A. Soslow
    • 6
  • Nadeem R. Abu-Rustum
    • 7
  • Hedvig Hricak
    • 1
  • Evis Sala
    • 1
  1. 1.Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkUSA
  3. 3.Department of RadiologyIuliu Hatieganu University of Medicine and PharmacyCluj-NapocaRomania
  4. 4.Department of Epidemiology and BiostatisticsMemorial Sloan Kettering Cancer CenterNew YorkUSA
  5. 5.Department of RadiologyInstitut Régional du Cancer de MontpellierMontpellierFrance
  6. 6.Department of PathologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
  7. 7.Gynecologic Service, Department of SurgeryMemorial Sloan Kettering Cancer CenterNew YorkUSA

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