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      Development of a tool for prediction of ovarian cancer in patients with adnexal masses: Value of plasma fibrinogen

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          Abstract

          Objective

          To develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.

          Study design

          We performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA) was then used to evaluate the clinical net benefit of the prediction model.

          Results

          Ovarian cancer including borderline tumours was found in 241 (26.6%) patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87–0.94). DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.

          Conclusion

          We confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.

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          Most cited references27

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests.

            We review the principles and practical application of receiver-operating characteristic (ROC) analysis for diagnostic tests. ROC analysis can be used for diagnostic tests with outcomes measured on ordinal, interval or ratio scales. The dependence of the diagnostic sensitivity and specificity on the selected cut-off value must be considered for a full test evaluation and for test comparison. All possible combinations of sensitivity and specificity that can be achieved by changing the test's cut-off value can be summarised using a single parameter; the area under the ROC curve. The ROC technique can also be used to optimise cut-off values with regard to a given prevalence in the target population and cost ratio of false-positive and false-negative results. However, plots of optimisation parameters against the selected cut-off value provide a more-direct method for cut-off selection. Candidates for such optimisation parameters are linear combinations of sensitivity and specificity (with weights selected to reflect the decision-making situation), odds ratio, chance-corrected measures of association (e. g. kappa) and likelihood ratios. We discuss some recent developments in ROC analysis, including meta-analysis of diagnostic tests, correlated ROC curves (paired-sample design) and chance- and prevalence-corrected ROC curves.
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              Regression models in clinical studies: determining relationships between predictors and response.

              Multiple regression models are increasingly being applied to clinical studies. Such models are powerful analytic tools that yield valid statistical inferences and make reliable predictions if various assumptions are satisfied. Two types of assumptions made by regression models concern the distribution of the response variable and the nature or shape of the relationship between the predictors and the response. This paper addresses the latter assumption by applying a direct and flexible approach, cubic spline functions, to two widely used models: the logistic regression model for binary responses and the Cox proportional hazards regression model for survival time data.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Project administrationRole: ResourcesRole: Writing – original draft
                Role: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – original draft
                Role: Data curationRole: InvestigationRole: Writing – original draft
                Role: Data curationRole: InvestigationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                24 August 2017
                2017
                : 12
                : 8
                : e0182383
                Affiliations
                [1 ] Department for Gynecology and Gynecologic Oncology, Gynecologic Cancer Unit, Comprehensive Cancer Centre, Medical University of Vienna, Vienna, Austria
                [2 ] Department of Urology, Medical University of Vienna, Vienna, Austria
                [3 ] Karl Landsteiner Institute for General Gynecology and Experimental Gynecologic Oncology, Vienna, Austria
                Suzhou University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-9773-6430
                http://orcid.org/0000-0003-2948-8129
                Article
                PONE-D-17-14612
                10.1371/journal.pone.0182383
                5570374
                28837575
                3bc1cb38-a7b5-497f-ad18-54e3d048341d
                © 2017 Seebacher et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 April 2017
                : 17 July 2017
                Page count
                Figures: 4, Tables: 4, Pages: 15
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Glycobiology
                Glycoproteins
                Fibrinogen
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Gynecological Tumors
                Ovarian Cancer
                Medicine and Health Sciences
                Surgical and Invasive Medical Procedures
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Ultrasound Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Ultrasound Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Ultrasound Imaging
                Medicine and Health Sciences
                Oncology
                Cancer Treatment
                Surgical Oncology
                Medicine and Health Sciences
                Clinical Medicine
                Clinical Oncology
                Surgical Oncology
                Medicine and Health Sciences
                Oncology
                Clinical Oncology
                Surgical Oncology
                Biology and Life Sciences
                Anatomy
                Body Fluids
                Blood
                Blood Plasma
                Medicine and Health Sciences
                Anatomy
                Body Fluids
                Blood
                Blood Plasma
                Biology and Life Sciences
                Physiology
                Body Fluids
                Blood
                Blood Plasma
                Medicine and Health Sciences
                Physiology
                Body Fluids
                Blood
                Blood Plasma
                Medicine and Health Sciences
                Oncology
                Cancer Treatment
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Malignant Tumors
                Custom metadata
                The DOI for accessing the minimal data set at www.fishare.com is: https://doi.org/10.6084/m9.figshare.5286310.v1.

                Uncategorized
                Uncategorized

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