Cargando…
Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers
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 algorit...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073859/ https://www.ncbi.nlm.nih.gov/pubmed/31973047 http://dx.doi.org/10.3390/jcm9020299 |
_version_ | 1783506715057061888 |
---|---|
author | Lycke, Maria Ulfenborg, Benjamin Kristjansdottir, Björg Sundfeldt, Karin |
author_facet | Lycke, Maria Ulfenborg, Benjamin Kristjansdottir, Björg Sundfeldt, Karin |
author_sort | Lycke, Maria |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7073859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70738592020-03-19 Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers Lycke, Maria Ulfenborg, Benjamin Kristjansdottir, Björg Sundfeldt, Karin J Clin Med Article 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. MDPI 2020-01-21 /pmc/articles/PMC7073859/ /pubmed/31973047 http://dx.doi.org/10.3390/jcm9020299 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lycke, Maria Ulfenborg, Benjamin Kristjansdottir, Björg Sundfeldt, Karin Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers |
title | Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers |
title_full | Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers |
title_fullStr | Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers |
title_full_unstemmed | Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers |
title_short | Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers |
title_sort | increased diagnostic accuracy of adnexal tumors with a combination of established algorithms and biomarkers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073859/ https://www.ncbi.nlm.nih.gov/pubmed/31973047 http://dx.doi.org/10.3390/jcm9020299 |
work_keys_str_mv | AT lyckemaria increaseddiagnosticaccuracyofadnexaltumorswithacombinationofestablishedalgorithmsandbiomarkers AT ulfenborgbenjamin increaseddiagnosticaccuracyofadnexaltumorswithacombinationofestablishedalgorithmsandbiomarkers AT kristjansdottirbjorg increaseddiagnosticaccuracyofadnexaltumorswithacombinationofestablishedalgorithmsandbiomarkers AT sundfeldtkarin increaseddiagnosticaccuracyofadnexaltumorswithacombinationofestablishedalgorithmsandbiomarkers |