Autor: |
Bandry, Kirollos Wagdy, Abou-Taleb, Hisham, Seifeldein, Gehan S., Taha, Mohamad Gaber, Qenawy, Omran Khodary |
Zdroj: |
Egyptian Journal of Radiology & Nuclear Medicine; 1/14/2022, Vol. 53 Issue 1, p1-8, 8p |
Abstrakt: |
Background: Postmenstrual spotting has recently been related to a discontinuation of the myometrium at the site of a previous cesarean section called "CS scar niche". There was no consensus regarding the gold standard method for the assessment of the niche. Recently, Magnetic resonance imaging (MRI) has shown promise in the evaluation of the niche. Our study aims to assess the role of MRI in the evaluation of the CS scar niche characters and its association with post-menstrual spotting. Results: A total of 65 patients with CS niche were prospectively included in this study and subdivided into two groups, according to presence or absence of postmenstrual spotting (Group A; 34 patients with postmenstrual spotting and Group B; 31 patients without spotting). All patients were examined using a 1.5 T MRI unit. CS scar niche volume was significantly higher among women with post-menstrual spotting (0.57 ± 0.07 vs. 0.07 ± 0.05 (cm3); P < 0.001). Also, women with post-menstrual spotting have significantly higher scar length (9.38 ± 3.06 vs. 5.02 ± 2.10 (mm); P < 0.001), scar depth (6.95 ± 3.16 vs. 3.23 ± 0.99 (mm); P < 0.001), scar width (15.78 ± 3.94 vs. 9.87 ± 1.84 (mm); P < 0.001) in comparison to those without post-menstrual spotting. Scar depth (> 7.4 mm) had 81% sensitivity and 97% specificity for prediction of post-menstrual spotting with overall accuracy was 88.7%. While scar width (> 12.8 mm) had 71% sensitivity and 97% specificity for prediction of post-menstrual spotting with overall accuracy was 83.3%. Scar volume (> 0.15 cm3) had 97% sensitivity and 100% specificity for prediction of post-menstrual spotting with overall accuracy was 98.4%. Conclusion: MRI measures (CS scar volume, depth, and width) are predictors for postmenstrual spotting in patients with CS scar niche, and scar volume is the most powerful predictor. [ABSTRACT FROM AUTHOR] |
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