Role of magnetic resonance imaging in evaluation of urogenital sinus malformations.

BACKGROUND

Persistent urogenital sinus (UGS) is a rare but complex congenital anomaly that poses significant challenges in pediatric reconstructive surgery. Accurate preoperative visualization of the urogenital anatomy is essential for optimal surgical planning. This study aims to evaluate the diagnostic utility of magnetic resonance imaging (MRI) in assessing persistent UGS, specifically the site of confluence, common channel length, urethral length, and to correlate MRI findings with the preoperative cystoscopy and surgical outcomes.

OBJECTIVES

To evaluate role of MRI in evaluation of urogenital sinus and associated anomalies helping in pre operative planning for surgical decision making and post operative evaluation.

MATERIALS AND

METHODS

This prospective study included 29 female pediatric patients with UGS anomalies who underwent pelvic MRI as part of their preoperative assessment. MRI findings were compared with intraoperative data. Parameters assessed included the length of the urethra, the common urogenital channel, and vaginal length, in addition to the identification of associated anomalies. Diagnostic performance metrics, including sensitivity, specificity, and accuracy, were calculated.

RESULTS

MRI identified UGS in 89.7 % of surgically confirmed cases, with radiological-surgical concordance observed in 25 out of 29 patients. Discordance was noted in 4 cases, including one misclassified "high" type UGS and three "very low" types not visualized on MRI. MRI showed a sensitivity of 89.7 % and an overall accuracy of 89.7 %. Significant correlations were found between UGS type and both common channel length (P = 0.005) and urethral length (P = 0.011). Associated anomalies were present in 68.9 % of patients, most commonly involving genital, spinal, and rectal systems.

CONCLUSION

MRI is a highly sensitive and accurate modality for the preoperative evaluation of UGS, offering detailed anatomical insights and enabling the detection of associated anomalies. Its integration into diagnostic workflows can improve surgical planning and patient outcomes.