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

  • Oncology
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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.

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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

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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.

Author information

Author notes

  1. Diana Feier

    Present address: Department of Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania

  2. Stephanie Nougaret

    Present address: Department of Radiology, Institut Régional du Cancer de Montpellier, Montpellier, France

Authors and Affiliations

  1. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yulia Lakhman, Joshua Chaim, Diana Feier, Stephanie Nougaret, Ramon E. Sosa, Hebert Alberto Vargas, Hedvig Hricak & Evis Sala

  2. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Harini Veeraraghavan

  3. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Debra A. Goldman & Chaya S. Moskowitz

  4. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Robert A. Soslow

  5. Gynecologic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Nadeem R. Abu-Rustum

Authors
  1. Yulia Lakhman
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Corresponding author

Correspondence to Yulia Lakhman.

Additional information

Yulia Lakhman and Harini Veeraraghavan contributed equally to this work.

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Lakhman, Y., Veeraraghavan, H., Chaim, J. et al. Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis. Eur Radiol 27, 2903–2915 (2017). https://doi.org/10.1007/s00330-016-4623-9

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  • DOI: https://doi.org/10.1007/s00330-016-4623-9

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