Erschienen in:
06.12.2023 | Gynecologic Oncology
Smooth muscle tumours of the uterus: MR imaging malignant predictive features—a 12-year analysis in a referral hospital in Portugal
verfasst von:
Patrícia Freitas, Teresa Resende-Neves, Pedro Lameira, Marta Costa, Paulo Dias, Juliana Filipe, Joana Ferreira, Ana Félix, Teresa Margarida Cunha
Erschienen in:
Archives of Gynecology and Obstetrics
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Ausgabe 4/2024
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Abstract
Purpose
To evaluate the magnetic resonance imaging (MRI) features that may help distinguish leiomyosarcomas from atypical leiomyomas (those presenting hyperintensity on T2-W images equal or superior to 50% compared to the myometrium).
Materials and methods
The authors conducted a retrospective single-centre study that included a total of 57 women diagnosed with smooth muscle tumour of the uterus, who were evaluated with pelvic MRI, between January 2009 and March 2020. All cases had a histologically proven diagnosis (31 Atypical Leiomyomas—ALM; 26 Leiomyosarcomas—LMS). The MRI features evaluated in this study included: age at presentation, dimension, contours, intra-tumoral haemorrhagic areas, T2-WI heterogeneity, T2-WI dark areas, flow voids, cyst areas, necrosis, restriction on diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) values, signal intensity and heterogeneity after contrast administration in T1-WI, presence and location of unenhanced areas. The association between the MRI characteristics and the histological subtype was evaluated using Chi-Square and ANOVA tests.
Results
The MRI parameters that showed a statistically significance correlation with malignant histology and thus most strongly associated with LMS were found to be: irregular contours (p < 0.001), intra-tumoral haemorrhagic areas (p = 0.028), T2-WI dark areas (p = 0.016), high signal intensity after contrast administration (p = 0.005), necrosis (p = 0.001), central location for unenhanced areas (p = 0.026), and ADC value lower than 0.88 × 10−3 mm2/s (p = 0.002).
Conclusion
With our work, we demonstrate the presence of seven MRI features that are statistically significant in differentiating between LMS and ALM.