Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis
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To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA).
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.
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).
Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible.
• 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.
KeywordsMagnetic Resonance Imaging Uterine Leiomyosarcoma Uterine Leiomyoma Atypical Uterine Leiomyoma Texture Analysis
Field of view
Gray-level co-occurrence matrices
Health Insurance Portability and Accountability Act
Magnetic resonance imaging
Negative predictive value
Positive predictive value
Regions of interest
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.
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