Prediction of ovarian masses in pregnant women

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Abstract

BACKGROUND: Ovarian masses during pregnancy are a common pathology, occurring in up to 3.2% of cases. However, data on recurrence after the removal of ovarian masses in women, which could help predict their potential occurrence, remain limited.

AIM: To assess the feasibility of predicting ovarian masses during pregnancy through a prognostic model based on data obtained using developed scoring systems.

MATERIALS AND METHODS: The study included 100 women in their second and third trimesters of pregnancy, divided into two groups: group 1 (main group) consisted of 50 pregnant women with ovarian masses that appeared for the first time (before the first delivery) or recurred (before the second delivery); group 2 (control group) included 50 pregnant women without ovarian pathology in either their first or subsequent pregnancies.

RESULTS: Two prognostic models were developed and tested on a random sample of 24 pregnant women with newly diagnosed or recurrent ovarian masses. The accuracy of the predictions was found to be acceptable, with a predictive accuracy of 83% in the first case and 62% in the second.

CONCLUSION: Prognostic models appear feasible for estimating the probability of both newly diagnosed and recurrent ovarian masses. This approach enhances diagnostic accuracy, improves prevention strategies for gynecological pathology, and aids in the selection of appropriate treatment and management strategies for patients with ovarian masses, even before ultrasound evaluation or surgical intervention.

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About the authors

Dinara A. Fatkullina

Bashkir State Medical University

Author for correspondence.
Email: mukhamadzhanova91@gmail.com
ORCID iD: 0000-0002-9615-2134
SPIN-code: 4661-4751

Graduate Student

Russian Federation, 3 Lenin st, Ufa, 450008

Alfiya G. Yashchuk

Bashkir State Medical University

Email: alfiya_galimovna@mail.ru
ORCID iD: 0000-0003-2645-1662
SPIN-code: 2607-9150

MD, Dr. Sci. (Medicine), Professor

Russian Federation, 3 Lenin st, Ufa, 450008

Ilnur Ir. Musin

Bashkir State Medical University

Email: ilnur-musin@yandex.ru
ORCID iD: 0000-0001-5520-5845
SPIN-code: 4829-1179

MD, Dr. Sci. (Medicine), Assistant Professor

Russian Federation, 3 Lenin st, Ufa, 450008

Eugeny M. Gareev

Bashkir State Medical University

Email: gem46@list.ru
ORCID iD: 0000-0002-6561-0892
SPIN-code: 9325-1326

Cand. Sci. (Biology), Assistant Professor

Russian Federation, 3 Lenin st, Ufa, 450008

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СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
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