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DOI 10.1016/j.crad.2021.10.022

Медицинская библиотека · Clinical radiology · 2021

MRI characteristics for differentiating mucinous borderline ovarian tumours from mucinous ovarian cancers.

Yang X Y, Li X, Ma F H, Li H M, Zhao S H, Li Y A, Qiang J W

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

AIM: To investigate the magnetic resonance imaging (MRI) characteristics that could differentiate mucinous borderline ovarian tumours (MBOTs) from mucinous ovarian cancers (MOCs).

MATERIALS AND

METHODS

MRI data from 75 patients with MBOTs and 38 patients with MOCs were reviewed retrospectively. The clinicopathological and MRI features, including age, bilaterality, maximum diameter (MD), shape, margin, configuration, cystic-solid interface, papillae, MD of the cyst walls and septa, MD of the solid components, number of cysts, honeycomb loculi, signal of the cystic and solid components, apparent diffusion coefficient (ADC) value and enhancement ratio of the solid components, peritoneal implants and ascites, were compared using univariable analysis and multivariable logistic regression analysis.

RESULTS

There were 76 MBOTs and 39 MOCs, and median patient age was 41 years (range 16-77 years) and 51 years (range 15-90 years), respectively (p=0.004). There were significant differences between MBOTs and MOCs regarding the presence of papillae (p=0.013), MD of the solid components (p=0.001), enhancement ratio of the solid components (p=0.003), ADC value (p<0.001), and ascites (p<0.001). The optimal cut-off ADC value was 1.16 × 10-3 mm2/s, with a sensitivity of 87.1%, a specificity of 83.3%, and an area under the curve (AUC) of 0.917.

CONCLUSION

Compared with MOCs, MBOTs had fewer papillae or solid components, lower enhancement ratio, higher ADC values, and were less likely to have moderate or massive ascites.

Для цитирования: Yang X Y, Li X, Ma F H, Li H M, Zhao S H, Li Y A, Qiang J W. MRI characteristics for differentiating mucinous borderline ovarian tumours from mucinous ovarian cancers.. Clinical radiology. 2021. DOI: 10.1016/j.crad.2021.10.022.

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