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Using logistic regression analysis in preliminary differential diagnosis of adnexal masses
  1. D. SZPUREK*,
  2. M. RAFAL*,
  3. S. AGATA and
  4. S. STEFAN*
  1. *Division of Gynecological Surgery Department of Gynecology and Obstetrics, Karol Marcinkowski University of Medical Sciences, Poznan, Poland
  2. †Institute of Mathematics and Medical Biostatistics University of Medical Sciences, Lublin, Poland
  1. Address correspondence and reprint requests to: Dariusz Szpurek, MD, PhD, Division of Gynecological Surgery, Department of Gynecology and Obstetrics, Karol Marcinkowski University of Medical Sciences, 33 Polna Street 60-535 Poznan, Poland. Email: dszpurek{at}


The aim of our study was to generate predictive model, which would allow to estimate the influence of analyzed parameters on predictive accuracy of differential diagnosis of adnexal masses and to evaluate prospectively diagnostic efficacy of the statistic model in the new set of patients. A total of 686 women diagnosed and surgically treated in the Gynecological and Obstetrical Teaching Hospital of University of Medical Sciences in Poznan, Poland, were recruited into the study. Preoperative diagnostics included gynecological examination, ultrasonographic evaluation, tumor Doppler blood flow analysis, and serum levels of CA125 and TPS. In order to find the best combination of features and to calculate the individual probability of the malignancy, stepwise logistic regression analysis with quasi-Newton estimation was applied. The essential part of the best prognostic model, described by foregoing variables, is as follows:

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The highest sensitivity and specificity for the obtained model were 87.84% and 93.74%, respectively. Prognostic model, constructed with the use of logistic regression analysis, is characterized by higher sensitivity and specificity than individually applied diagnostic tests. Prospective evaluation of this model application in a larger group of patients with adnexal masses will enable precise assessment of its objective clinical usefulness.

  • logistic regression
  • ovarian cancer
  • ovarian tumors
  • prognostic model

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