In:
The British Journal of Radiology, British Institute of Radiology, Vol. 94, No. 1125 ( 2021-09-01), p. 20210115-
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
0007-1285
,
1748-880X
DOI:
10.1259/bjr.20210115
Sprache:
Englisch
Verlag:
British Institute of Radiology
Publikationsdatum:
2021
ZDB Id:
1468548-6
