DIFFERENTIAL DIAGNOSIS OF BENIGN, BORDERLINE, AND MALIGNANT OVARIAN MASSES IN PREGNANT WOMEN, BY USING LOGISTIC REGRESSION MODELS


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Resumo

Objective. To enhance the accuracy of differential diagnosis of benign, borderline, and malignant ovarian tumors in pregnant women, by building a logistic regression model. Material and methods. Regression logistic models were built to demonstrate that one can differentiate true tumors from tumor-like masses and benign neoplasms from malignant ones in 223 pregnant women on the basis of ultrasound signs of ovarian tumor-like masses and tumors. Results. While diagnosing benign ovarian tumors in pregnant women, the sensitivity and specificity of the model were 97 and 95%, respectively. While diagnosing borderline and malignant tumors, the sensitivity of the model was 100% and its specificity was 92.3% with a total accuracy of 92.8%. Conclusion. The performed studies have demonstrated that the authors’ regression logistic models can help a practitioner make timely a differential diagnosis of ovarian tumors in pregnant women, thus using their rational treatment policy.

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Sobre autores

A. GERASIMOVA

Center for Family Planning and Reproduction, Moscow Healthcare Department

Moscow

S. SHVYREV

Russian State Medical University, Russian Agency for Health Care

Moscow

K. STEPANOV

Russian State Medical University, Russian Agency for Health Care

Moscow

A. GUS

Academician V.I. Kulakov Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health and Social Development of Russia

Moscow

P. KLIMENKO

Russian State Medical University, Russian Agency for Health Care

Moscow

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