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The combination of ovarian volume and outline has better diagnostic accuracy than prostate-specific antigen (PSA) concentrations in women with polycystic ovarian syndrome (PCOs).

Eur J Obstet Gynecol Reprod Biol. 2014 Aug;179:32-5. doi: 10.1016/j.ejogrb.2014.05.006. Epub 2014 May 20.

Bili AE1, Dampala K2, Iakovou I3, Tsolakidis D2, Giannakou A4, Tarlatzis BC2.

Eur J Obstet Gynecol Reprod Biol
AbstractOBJECTIVES: The aim of this study was to determine the performance of prostate specific antigen (PSA) and ultrasound parameters, such as ovarian volume and outline, in the diagnosis of polycystic ovary syndrome (PCOS).STUDY DESIGN: This prospective, observational, case-controlled study included 43 women with PCOS, and 40 controls. Between day 3 and 5 of the menstrual cycle, fasting serum samples were collected and transvaginal ultrasound was performed. The diagnostic performance of each parameter [total PSA (tPSA), total-to-free PSA ratio (tPSA:fPSA), ovarian volume, ovarian outline] was estimated by means of receiver operating characteristic (ROC) analysis, along with area under the curve (AUC), threshold, sensitivity, specificity as well as positive (+) and negative (-) likelihood ratios (LRs). Multivariate logistical regression models, using ovarian volume and ovarian outline, were constructed.RESULTS: The tPSA and tPSA:fPSA ratio resulted in AUC of 0.74 and 0.70, respectively, with moderate specificity/sensitivity and insufficient LR+/- values. In the multivariate logistic regression model, the combination of ovarian volume and outline had a sensitivity of 97.7% and a specificity of 97.5% in the diagnosis of PCOS, with +LR and -LR values of 39.1 and 0.02, respectively.CONCLUSIONS: In women with PCOS, tPSA and tPSA:fPSA ratio have similar diagnostic performance. The use of a multivariate logistic regression model, incorporating ovarian volume and outline, offers very good diagnostic accuracy in distinguishing women with PCOS patients from controls.Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Full Text Source: Elsevier Science
PMID:24965976 |

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