Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain.

To define and weight the preoperative CT findings for ovarian torsion and to develop an integrated nomogram for estimating the probability of ovarian torsion in women with ovarian lesion and pelvic pain. This retrospective study included 218 women with surgically resected ovarian lesions who underwent preoperative contrast-enhanced CT for pelvic pain from January 2014 to February 2019. Significant imaging findings for torsion were extracted using regression analyses and a regression coefficient-based nomogram was constructed. The diagnostic performance with sensitivity, specificity, and accuracy of the significant imaging findings and the nomogram were assessed. A total of 255 ovarian lesions (123 lesions with torsion and 132 lesions without torsion) were evaluated. Multivariable regression analysis showed that whirl sign (odds ratio [OR] 11.000; p < 0.001), tubal thickening (OR 4.621; p = 0.001), unusual location of ovarian lesion (OR 2.712; p = 0.020), and hemorrhagic component within adnexal lesion (OR 2.537; p = 0.028) were independent significant parameters predicting ovarian torsion. Tubal thickening showed the highest sensitivity (91.1%) and whirl sign showed the highest specificity (94.7%). When probabilities of ovarian torsion of 0.5 or more in the nomogram were diagnosed as ovarian torsion, sensitivity, specificity, and accuracy of the nomogram were 78.1%, 91.7%, and 85.1%, respectively. The whirl sign, tubal thickening, unusual location of ovarian lesion, and hemorrhagic component within adnexal lesion, and an integrated nomogram derived from these significant findings can be useful for predicting ovarian torsion.

Citation

Jeong Ah Hwang, Hyeong Cheol Shin, Seung Soo Kim, Nam Hun Heo, Seo-Youn Choi, Ji Eun Lee, Sunyoung Lee. Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain. Abdominal radiology (New York). 2021 Mar;46(3):1137-1147

Mesh Tags

PMID: 32935259

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