Diagnostic Sensitivity and Specificity of Biomarkers in Combination with Ultrasonographic Findings and Clinical Symptoms in Ovarian Cancer. Summary of the Doctoral Thesis

Abstract

Ovarian cancer is the second most common morbidity among gynecological cancers. In the period from 2003 to 2009 in Latvia there were 283-340 ovarian cancer patients diagnosed each year with mortality rates of 58,6-70,7%. Five year survival rate reaches 80% when disease is diagnosed in the early stages, but only 30%, when detected in the late stages. It has been proven that better treatment results can be achieved when ovarian cancer patients are centralised and treated by gynecological oncologists, therefore it is important to establish recommendations for gynecologists and physicians in ovarian tumor differentialdiagnosis. Aim of the study is to develop ovarian cancer detection algorithm with the most sensitive and specific biomarker combination together with clinical symptoms and ultrasonographic findings. In the prospective case-control study 83 patients with ovarian cancer, 77 patients with benigh ovarian tumors and 82 healthy, age-matched women in the control group were involved. Sensitivity and specificity of ovarian cancer symptom index were analyzed separately and in the conjuction with the biomarker concentrations. Also sensitvity and specificity for different methodologies of malignancy risk index calculations and diagnostic accuracy of single biomarkers (apolipoprotein A1, transthyretin, transferrin, beta-2-microglobulin, human epidydimis secretory protein 4 (HE4) and ovarian cancer associated antigen CA125) and their combinations were analyzed. A new diagnostic algorithm for patients with ovarian tumors was developed. Newly established ovarian cancer detection algorithm is more sensitive and specific in comparison to conventional diagnostic tests and has been evaluated in independent patient populations from „Danish Pelvic Mass Project” and Asia Pacific Ovarian cancer biomarker research group study. Recommendations on ovarian cancer symptom index, malignancy risk index and novel ovarian cancer diagnostic algorithm application in clinical practice were developed.