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Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers
Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg and Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. (Translationell Bioinformatik, Translational Bioinformatics)ORCID iD: 0000-0001-9242-4852
Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg and Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg and Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
2020 (English)In: Journal of clinical medicine, ISSN 2077-0383, Vol. 9, no 2, article id E299Article in journal (Refereed) Published
Abstract [en]

Ovarian cancer is the most lethal gynecologic cancer. Pre-diagnostic testing lacks sensitivity and specificity, and surgery is often the only way to secure the diagnosis. Exploring new biomarkers is of great importance, but the rationale of combining validated well-established biomarkers and algorithms could be a more effective way forward. We hypothesized that we can improve differential diagnostics and reduce false positives by combining (a) risk of malignancy index (RMI) with serum HE4, (b) risk of ovarian malignancy algorithm (ROMA) with a transvaginal ultrasound score or (c) adding HE4 to CA125 in a simple algorithm. With logistic regression modeling, new algorithms were explored and validated using leave-one-out cross validation. The analyses were performed in an existing cohort prospectively collected prior to surgery, 2013-2016. A total of 445 benign tumors and 135 ovarian cancers were included. All presented models improved specificity at cut-off compared to the original algorithm, and goodness of fit was significant (p < 0.001). Our findings confirm that HE4 is a marker that improves specificity without hampering sensitivity or diagnostic accuracy in adnexal tumors. We provide in this study "easy-to-use" algorithms that could aid in the triage of women to the most appropriate level of care when presenting with an unknown ovarian cyst or suspicious ovarian cancer.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 9, no 2, article id E299
Keywords [en]
CA125, HE4, RMI, ROMA, algorithms, diagnosis, ovarian neoplasm
National Category
Cancer and Oncology
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:his:diva-18341DOI: 10.3390/jcm9020299ISI: 000518823000012PubMedID: 31973047OAI: oai:DiVA.org:his-18341DiVA, id: diva2:1416685
Available from: 2020-03-25 Created: 2020-03-25 Last updated: 2020-03-25Bibliographically approved

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