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Diagnostic interpretation of non-contrast qualitative MR imaging features for characterisation of uterine leiomyosarcoma.
Sahin, Hilal; Smith, Janette; Zawaideh, Jeries Paolo; Shakur, Amreen; Carmisciano, Luca; Caglic, Iztok; Bruining, Annemarie; Jimenez-Linan, Mercedes; Freeman, Sue; Addley, Helen.
Afiliação
  • Sahin H; Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
  • Smith J; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
  • Zawaideh JP; Tepecik Training and Research Hospital, University of Health Sciences, Izmir, Turkey.
  • Shakur A; Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Carmisciano L; Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Caglic I; Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Bruining A; Department of Health Sciences (DISSAL), Biostatistics section, University of Genoa, Genoa, Italy.
  • Jimenez-Linan M; Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Freeman S; Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Addley H; Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Br J Radiol ; 94(1125): 20210115, 2021 Sep 01.
Article em En | MEDLINE | ID: mdl-34111973
OBJECTIVE: To assess the value of non-contrast MRI features for characterisation of uterine leiomyosarcoma (LMS) and differentiation from atypical benign leiomyomas. METHODS: This study included 57 atypical leiomyomas and 16 LMS which were referred pre-operatively for management review to the specialist gynaeoncology multidisciplinary team meeting. Non-contrast MRIs were retrospectively reviewed by five independent readers (three senior, two junior) and a 5-level Likert score (1-low/5-high) was assigned to each mass for likelihood of LMS. Evaluation of qualitative and quantitative MRI features was done using uni- and multivariable regression analysis. Inter-reader reliability for the assessment of MRI features was calculated by using Cohen's κ values. RESULTS: In the univariate analysis, interruption of the endometrial interface and irregular tumour shape had the highest odds ratios (ORs) (64.00, p < 0.001 and 12.00, p = 0.002, respectively) for prediction of LMS. Likert score of the mass was significant in prediction (OR, 3.14; p < 0.001) with excellent reliability between readers (ICC 0.86; 95% CI, 0.76-0.92). The post-menopausal status, interruption of endometrial interface and thickened endometrial stripe were the most predictive independent variables in multivariable estimation of the risk of leiomyosarcoma with an accuracy of 0.88 (95%CI, 0.78-0.94). CONCLUSION: At any level of expertise as a radiologist reader, the loss of the normal endometrial stripe (either thickened or not seen) in a post-menopausal patient with a myometrial mass was highly likely to be LMS. ADVANCES IN KNOWLEDGE: This study demonstrates the potential utility of non-contrast MRI features in characterisation of LMS over atypical leiomyomas, and therefore influence on optimal management of these cases.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Uterinas / Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Leiomiossarcoma Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Uterinas / Imageamento por Ressonância Magnética / Interpretação de Imagem Assistida por Computador / Leiomiossarcoma Idioma: En Ano de publicação: 2021 Tipo de documento: Article