Differentiation of uterine leiomyosarcoma from atypical leiomyoma: Diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis Journal Article


Authors: Lakhman, Y.; Veeraraghavan, H.; Chaim, J.; Feier, D.; Goldman, D. A.; Moskowitz, C. S.; Nougaret, S.; Sosa, R. E.; Vargas, H. A.; Soslow, R. A.; Abu-Rustum, N. R.; Hricak, H.; Sala, E.
Article Title: Differentiation of uterine leiomyosarcoma from atypical leiomyoma: Diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis
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. © 2016, European Society of Radiology.
Keywords: magnetic resonance imaging; uterine leiomyosarcoma; texture analysis; atypical uterine leiomyoma; uterine leiomyoma
Journal Title: European Radiology
Volume: 27
Issue: 7
ISSN: 0938-7994
Publisher: Springer  
Date Published: 2017-07-01
Start Page: 2903
End Page: 2915
Language: English
DOI: 10.1007/s00330-016-4623-9
PROVIDER: scopus
PMCID: PMC5459669
PUBMED: 27921159
DOI/URL:
Notes: Article -- Export Date: 3 July 2017 -- Source: Scopus
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MSK Authors
  1. Joshua Chaim
    17 Chaim
  2. Yuliya Lakhman
    32 Lakhman
  3. Evis Sala
    94 Sala
  4. Chaya S. Moskowitz
    172 Moskowitz
  5. Robert Soslow
    641 Soslow
  6. Hedvig Hricak
    330 Hricak
  7. Debra Alyssa Goldman
    92 Goldman
  8. Ramon Elias Sosa
    16 Sosa
  9. Diana Sorina Feier
    3 Feier