PL EN


Journal
2014 | 3(45) | 151-164
Article title

The use of logistic regression in the ovarian cancer diagnostics

Content
Title variants
Languages of publication
EN
Abstracts
EN
In the present elaboration an attempt has been made to build the logit model which makes it possible to specify the probability of diagnosing the ovarian carcinoma in the female patients with pathological lesion in the ovary. Based on sampling of 210 patients treated and diagnosed at the Teaching Hospital of Operative Gynaecology and Gynaecological Oncology of Women and Girls of the Pomeranian Medical University, the evaluations of the parameters of two logit models were determined and the estimation of the quality of obtained models was made. The obtained results may contribute to supporting of the ovarian cancer diagnostics.
Journal
Year
Issue
Pages
151-164
Physical description
References
  • Cramer J.S., 2003, Logit Models from Economics and Other Fields, Cambridge University Press, Cambridge. DeLong E.R., DeLong D.M., Clarke-Pearson D.L., 1988, Comparing the areas under two or more correlated receiver opera ng curves: A nonparametric approach, Biometrics, vol. 44, pp. 837–845.
  • Dobosz M., 2004, Wspomagana komputerowo statystyczna analiza wyników badań, Akademicka Oficyna Wydawnicza EXIT, Warszawa, p. 260.
  • Gruszczyński M., Podgórska M., 1996, Ekonometria, Oficyna Wydawnicza Szkoły Głównej Handlowej, Warszawa, pp. 139–141.
  • Hellström I., Raycraft J., Hayden-Ledbetter M. et al., 2003, The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma, Cancer Res, vol. 63(13), pp. 3695–700.
  • Hosmer D.W., Lemenshow S., May S., 2008, Applied Survival Analysis: Regression Modeling of Time to Event Data, John Wiley & Sons, New York.
  • Hosmer D.W., Lemenshow S., 2004, Applied Logistic Regression, John Wiley & Sons, New York.
  • Kleinbaum D.G., Klein M., 2002, Logistic Regression, Springer, New York.
  • Maddala G.S., 2001, Introduction to Econometrics, 3rd ed. John Wiley & Sons.
  • Montagnana M., Danese E., Ruzzenente O. et al., 2011, The ROMA (Rik of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Clin Chem Lab Med., vol. 49(3), pp. 521–5.
  • Moore R.G., Jabre-Raughley M., Brown A.K. et al., 2010, Comparison of a novel multiple marker assay vs the Risk of Malignancy Index for the prediction of epithelial ovarian cancer in patients with a pelvic mass, Am J Obstet Gynecol, vol. 203(3), p. 228, e1-6.
  • Moore G., Miller M.C., Disilvestro P. et al., 2011, Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass, Obstet Gynecol, vol. 118 (2 Pt 1), pp. 280–8.
  • Nolen B., Velikokhatnaya L., Marrangoni A. et al., 2010, Serum biomarker panels for the discrimination of benign from malignant cases in patients with the adnexal masses, Gynecol Oncol, vol. 117, pp. 440–445. Stanisz A., 2007, Przystępny kurs z zastosowaniem Statistica PL na przykładach z medycyny. Statsoft, Kraków.
  • Zurawski V.R. Jr., Knapp R.C., Einhorn M. et al., 1988, An initial analysis of preoperative serum CA 125 levels in patients with early stage ovarian carcinoma, Gynecol Oncol, vol. 30, pp. 7–14. Zweig M.H., Campbell G., 1993, Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine, Clinical Chemistry, vol. 39, pp. 561–577.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-f6cbd649-a39d-4347-861c-ad77e1dcd141
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.