| Added by | mollevi |
|---|---|
| Group name | EquipeMY |
| Item Type | Journal Article |
| Title | Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis |
| Creator | Lakhman et al. |
| Author | Yulia Lakhman |
| Author | Harini Veeraraghavan |
| Author | Joshua Chaim |
| Author | Diana Feier |
| Author | Debra A. Goldman |
| Author | Chaya S. Moskowitz |
| Author | Stephanie Nougaret |
| Author | Ramon E. Sosa |
| Author | Hebert Alberto Vargas |
| Author | Robert A. Soslow |
| Author | Nadeem R. Abu-Rustum |
| Author | Hedvig Hricak |
| Author | Evis Sala |
| 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. |
| Publication | European Radiology |
| Volume | 27 |
| Issue | 7 |
| Pages | 2903-2915 |
| Date | Jul 2017 |
| Journal Abbr | Eur Radiol |
| Language | eng |
| DOI | 10.1007/s00330-016-4623-9 |
| ISSN | 1432-1084 |
| Short Title | Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma |
| Library Catalog | PubMed |
| Extra | PMID: 27921159 PMCID: PMC5459669 |
| Tags | Adolescent, Adult, Aged, Aged, 80 and over, Atypical Uterine Leiomyoma, clinic, Diagnosis, Differential, Feasibility Studies, Female, Humans, Leiomyoma, Leiomyosarcoma, Magnetic Resonance Imaging, Middle Aged, Reproducibility of Results, Retrospective Studies, Texture Analysis, Uterine Leiomyoma, Uterine Leiomyosarcoma, Uterine Neoplasms, Young Adult |
| Date Added | 2018/11/13 - 17:35:29 |
| Date Modified | 2019/05/21 - 13:34:17 |
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