Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.
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 surger...
| Publicado en: | European Radiology Vol. 27; no. 7; pp. 2903 - 2916 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123476763&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123476763 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2017 vid: 27 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 123476763 123476763 144066219 NLM27921159 123476763 10.1007/s00330-016-4623-9 NLM27921159 123476763 ppf: 2903 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis. aug: au: Lakhman, Yulia Veeraraghavan, Harini Chaim, Joshua Feier, Diana Goldman, Debra Moskowitz, Chaya Nougaret, Stephanie Sosa, Ramon Vargas, Hebert Soslow, Robert Abu-Rustum, Nadeem Hricak, Hedvig Sala, Evis Goldman, Debra A Moskowitz, Chaya S Sosa, Ramon E Vargas, Hebert Alberto Soslow, Robert A Abu-Rustum, Nadeem R affil: Department of Radiology , Memorial Sloan Kettering Cancer Center , New York USA sug: subj: Uterine Neoplasms Pathology Magnetic Resonance Imaging Methods Leiomyoma Pathology Leiomyosarcoma Diagnosis Aged Young Adult Pilot Studies Aged, 80 and Over Female Diagnosis, Differential Middle Age Adult Reproducibility of Results Retrospective Design Adolescence Funding Source Human Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Adult: 19-44 years Adolescent: 13-18 years Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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