Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model : A development and validation study
Reijnen, Casper (Radboud University Medical Center)
Gogou, Evangelia (Radboud University)
Visser, Nicole C. M. (Radboud University Medical Center)
Engerud, Hilde (Haukeland University Hospital (Bergen, Noruega))
Ramjith, Jordache (Radboud University Medical Center)
van der Putten, Louis J. M. (Radboud University Medical Center)
van de Vijver, Koen (Universitair Ziekenhuis Gent)
Santacana, Maria (Hospital Arnau de Vilanova (Lleida, Catalunya))
Bronsert, Peter (University Medical Center, Freiburg, Germany)
Bulten, Johan (Radboud University Medical Center)
Hirschfeld, Marc (University Medical Center, Freiburg, Germany)
Colás Ortega, Eva (Universitat Autònoma de Barcelona. Departament de Pediatria, Obstetrícia i Ginecologia i Medicina Preventiva i Salut Pública)
Gil-Moreno, Antonio 1965- (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Reques, Armando (Hospital Universitari Vall d'Hebron)
Mancebo, Gemma (Hospital del Mar (Barcelona, Catalunya))
Krakstad, Camilla (Haukeland University Hospital (Bergen, Noruega))
Trovik, Jone (Haukeland University Hospital (Bergen, Noruega))
Haldorsen, Ingfrid S. (University of Bergen)
Huvila, Jutta (University of Turku)
Koskas, Martin (Bichat-Claude Bernard Hospital, Paris)
Weinberger, Vit (University Hospital Brno (República Txeca))
Bednarikova, Marketa (University Hospital Brno (República Txeca))
Hausnerova, Jitka (University Hospital Brno (República Txeca))
van der Wurff, Anneke A. M. (Elisabeth-TweeSteden Hospital, Tilburg)
Matias-Guiu, Xavier (Hospital Arnau de Vilanova (Lleida, Catalunya))
Amant, Frederic (The Netherlands Cancer Institute (Amsterdam, Països Baixos))
Massuger, Leon F. A. G. (Radboud University Medical Center)
Snijders, Marc P. L. M. (Canisius-Wilhelmina Hospital)
Küsters-Vandevelde, Heidi V. N. (Canisius-Wilhelmina Hospital)
Lucas, Peter J. F. (University of Twente)
Pijnenborg, Johanna M. A. (Radboud University Medical Center)

Data:	2020
Resum:	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. 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. 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. Casper Reijnen and co-workers report on risk prediction in endometrial cancer.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	PLoS Medicine, Vol. 17 (may 2020) , ISSN 1549-1676

DOI: 10.1371/journal.pmed.1003111
PMID: 32413043

19 p, 1.5 MB

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