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

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dc.contributor.advisorDoniņa, Simona
dc.contributor.authorMačuks, Ronalds
dc.date.accessioned2023-08-08T12:31:25Z
dc.date.available2023-08-08T12:31:25Z
dc.date.issued2012
dc.descriptionResearch was carried out at Riga East University Hospital. Defence: on 25th of October 2012, at 15:00 o’clock, at the Specialized Surgery Council meeting of Riga Stradins University, Hippocrates lecture-room, 16 Dzirciema Street, Riga.en
dc.description.abstractOvarian 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.en
dc.description.sponsorshipThesis have been prepared with support of European Union project “Support for Doctoral Students in Acquiring Study Programme and Acquisition of the Scientific degree in Riga Stradins University”en
dc.formatElectronic
dc.identifier.citationMačuks, R. 2012. Diagnostic Sensitivity and Specificity of Biomarkers in Combination with Ultrasonographic Findings and Clinical Symptoms in Ovarian Cancer: Summary of the Doctoral Thesis: Speciality – Gynaecology and Obstetrics. Rīga: Rīga Stradiņš University. https://doi.org/10.25143/prom-rsu_2012-15_dtsen
dc.identifier.doihttps://doi.org/10.25143/prom-rsu_2012-15_dts
dc.identifier.other1–52
dc.identifier.urihttps://dspace.rsu.lv/jspui/handle/123456789/14361
dc.language.isoen
dc.publisherRīga Stradiņš Universityen
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectSummary of the Doctoral Thesisen
dc.subject.otherSector – Medicine; Speciality – Gynaecology and Obstetricsen
dc.titleDiagnostic Sensitivity and Specificity of Biomarkers in Combination with Ultrasonographic Findings and Clinical Symptoms in Ovarian Cancer. Summary of the Doctoral Thesisen
dc.title.alternativeBiomarķieri olnīcu vēža diagnostikā, to jutība un specifiskums kombinācijā ar ultrasonogrāfisko atradi un klīniskiem simptomiem. Promocijas darba kopsavilkumslv_LV
dc.typeinfo:eu-repo/semantics/other

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