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      Just how accurate are the major risk stratification systems for early-stage endometrial cancer?

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          Abstract

          Background:

          To compare the accuracy of five major risk stratification systems (RSS) in classifying the risk of recurrence and nodal metastases in early-stage endometrial cancer (EC).

          Methods:

          Data of 553 patients with early-stage EC were abstracted from a prospective multicentre database between January 2001 and December 2012. The following RSS were identified in a PubMed literature search and included the Post Operative Radiation Therapy in Endometrial Carcinoma (PORTEC-1), the Gynecologic Oncology Group (GOG)-99, the Survival effect of para-aortic lymphadenectomy (SEPAL), the ESMO and the ESMO-modified classifications. The accuracy of each RSS was evaluated in terms of recurrence-free survival (RFS) and nodal metastases according to discrimination.

          Results:

          Overall, the ESMO -modified RSS provided the highest discrimination for both RFS and for nodal metastases with a concordance index (C-index) of 0.73 (95% CI, 0.70–0.76) and an area under the curve (AUC) of 0.80 (0.78–0.72), respectively. The other RSS performed as follows: the PORTEC1, GOG-99, SEPAL, ESMO classifications gave a C-index of 0.68 (0.66–0.70), 0.65 (0.63–0.67), 0.66 (0.63–0.69), 0.71 (0.68–0.74), respectively, for RFS and an AUC of 0.69 (0.66–0.72), 0.69 (0.67–0.71), 0.68 (0.66–0.70), 0.70 (0.68–0.72), respectively, for node metastases.

          Conclusions:

          None of the five major RSS showed high accuracy in stratifying the risk of recurrence or nodal metastases in patients with early-stage EC, although the ESMO-modified classification emerged as having the highest power of discrimination for both parameters. Therefore, there is a need to revisit existing RSS using additional tools such as biological markers to better stratify risk for these patients.

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          Most cited references26

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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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            Time-dependent ROC curves for censored survival data and a diagnostic marker.

            ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.
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              Survival model predictive accuracy and ROC curves.

              The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
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                Author and article information

                Journal
                Br J Cancer
                Br. J. Cancer
                British Journal of Cancer
                Nature Publishing Group
                0007-0920
                1532-1827
                03 March 2015
                12 February 2015
                : 112
                : 5
                : 793-801
                Affiliations
                [1 ]Department of Obstetrics and Gynaecology, Tenon University Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), University Pierre and Marie Curie, Institut Universitaire de Cancérologie (IUC) , Paris 6, France
                [2 ]INSERM UMR S 707, 'Epidemiology, Information Systems, Modeling', University Pierre and Marie Curie , Paris 6, France
                [3 ]Department of Gynecological Surgery, Jeanne de Flandre University Hospital , Lille, France
                [4 ]Department of Radiation Oncology, Tenon University Hospital, University Pierre and Marie Curie , Paris 6, France
                [5 ]Centre de lutte contre le cancer Georges François Leclerc , Dijon, France
                [6 ]Department of Obstetrics and Gynaecology, Institute Alix de Champagne University Hospital , Reims, France
                [7 ]Department of Obstetrics and Gynecology, Centre Hospitalier Intercommunal , Créteil, France
                [8 ]INSERM UMR S 938, University Pierre et Marie Curie , Paris 6, France
                Author notes
                Article
                bjc201535
                10.1038/bjc.2015.35
                4453957
                25675149
                7cb55820-37b0-4768-ab4c-d1ebbb2ccca9
                Copyright © 2015 Cancer Research UK

                From twelve months after its original publication, this work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                History
                : 06 October 2014
                : 06 January 2015
                : 12 January 2015
                Categories
                Clinical Study

                Oncology & Radiotherapy
                endometrial cancer,prediction,stratification,recurrence,lymph node
                Oncology & Radiotherapy
                endometrial cancer, prediction, stratification, recurrence, lymph node

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