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dc.contributor.authorReijnen, Casper
dc.contributor.authorGogou, Evangelia
dc.contributor.authorVisser, Nicole C.M.
dc.contributor.authorEngerud, Hilde
dc.contributor.authorRamjith, Jordache
dc.contributor.authorVan Der Putten, Louis J.M.
dc.contributor.authorVan De Vijver, Koen
dc.contributor.authorSantacana, Maria
dc.contributor.authorBronsert, Peter
dc.contributor.authorBulten, Johan
dc.contributor.authorHirschfeld, Marc
dc.contributor.authorColas, Eva
dc.contributor.authorGil-Moreno, Antonio
dc.contributor.authorReques, Armando
dc.contributor.authorMancebo, Gemma
dc.contributor.authorKrakstad, Camilla
dc.contributor.authorTrovik, Jone
dc.contributor.authorHaldorsen, Ingfrid S.
dc.contributor.authorHuvila, Jutta
dc.contributor.authorKoskas, Martin
dc.contributor.authorWeinberger, Vit
dc.contributor.authorBednarikova, Marketa
dc.contributor.authorHausnerova, Jitka
dc.contributor.authorVan Der Wurff, Anneke A. M.
dc.contributor.authorMatias-Guiu, Xavier
dc.contributor.authorAmant, Frédéric
dc.contributor.authorMassuger, Leon F.A.G.
dc.contributor.authorSnijders, Marc P.L.M.
dc.contributor.authorKüsters-Vandevelde, Heidi V.N.
dc.contributor.authorLucas, Peter J. F.
dc.contributor.authorPijnenborg, Johanna M.A.
dc.date.accessioned2021-05-03T13:37:48Z
dc.date.available2021-05-03T13:37:48Z
dc.date.created2020-11-29T15:49:09Z
dc.date.issued2020
dc.PublishedNature Methods. 2020, 17 (5), .
dc.identifier.issn1548-7091
dc.identifier.urihttps://hdl.handle.net/11250/2753327
dc.description.abstractBackground Bayesian networks (BNs) are machine-learning–based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. Methods and findings Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58–71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59–74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60–73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76–0.88) for LNM and 0.82 (95% CI 0.77–0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78–0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design. Conclusions In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.en_US
dc.language.isoengen_US
dc.publisherPLOSen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titlePreoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation studyen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2020 The Authorsen_US
dc.source.articlenumbere1003111en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1371/journal.pmed.1003111
dc.identifier.cristin1853775
dc.source.journalPLOS Medicineen_US
dc.source.4017
dc.source.145
dc.identifier.citationPLOS Medicine. 2020, 17 (5): e1003111en_US
dc.source.volume17en_US
dc.source.issue5en_US


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