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Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study

C. Reijnen, E. Gogou, NCM. Visser, H. Engerud, J. Ramjith, LJM. van der Putten, K. van de Vijver, M. Santacana, P. Bronsert, J. Bulten, M. Hirschfeld, E. Colas, A. Gil-Moreno, A. Reques, G. Mancebo, C. Krakstad, J. Trovik, IS. Haldorsen, J....

. 2020 ; 17 (5) : e1003111. [pub] 20200515

Language English Country United States

Document type Journal Article, Multicenter Study, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc20025008

BACKGROUND: 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.

Biomedical Research Group in Gynecology Vall Hebron Institute of Research Universitat Autònoma de Barcelona CIBERONC Barcelona Spain

Biomedical Research Group in Gynecology Vall Hebron Institute of Research Universitat Autònoma de Barcelona CIBERONC Barcelona Spain Gynecological Department Vall Hebron University Hospital CIBERONC Barcelona Spain

Centre for Cancer Biomarkers Department of Clinical Science University of Bergen Bergen Norway Mohn Medical Imaging and Visualization Centre Department of Radiology Haukeland University Hospital Bergen Norway

Department for Health Evidence Radboud University Medical Center Nijmegen the Netherlands

Department of Computing Sciences Radboud University Nijmegen The Netherlands

Department of Data Science University of Twente Enschede The Netherlands

Department of Gynecology and Obstetrics University Hospital in Brno and Masaryk University Brno Czech Republic

Department of Internal Medicine Hematology and Oncology University Hospital Brno and Masaryk University Brno Czech Republic

Department of Obstetrics and Gynaecology Canisius Wilhelmina Hospital Nijmegen The Netherlands

Department of Obstetrics and Gynaecology Radboud University Medical Center Nijmegen The Netherlands

Department of Obstetrics and Gynaecology Radboud University Medical Center Nijmegen The Netherlands Department of Obstetrics and Gynaecology Canisius Wilhelmina Hospital Nijmegen The Netherlands

Department of Obstetrics and Gynecology Haukeland University Hospital Bergen Norway Centre for Cancer Biomarkers Department of Clinical Science University of Bergen Bergen Norway

Department of Obstetrics and Gynecology Hospital del Mar PSMAR Barcelona Spain

Department of Obstetrics and Gynecology University Medical Center Freiburg Germany Institute of Veterinary Medicine Georg August University Goettingen Germany

Department of Oncology KU Leuven Leuven Belgium Center for Gynecologic Oncology Amsterdam Netherlands Cancer Institute and Amsterdam University Medical Center The Netherlands

Department of Pathology and Molecular Genetics and Research Laboratory Hospital Universitari Arnau de Vilanova University of Lleida IRBLleida CIBERONC Lleida Spain

Department of Pathology Canisius Wilhelmina Hospital Nijmegen The Netherlands

Department of Pathology Elisabeth TweeSteden Hospital Tilburg The Netherlands

Department of Pathology Ghent University Hospital Cancer Research Institute Ghent Ghent Belgium

Department of Pathology Radboud University Medical Center Nijmegen The Netherlands

Department of Pathology University Hospital Brno and Masaryk University Brno Czech Republic

Department of Pathology University of Turku Turku Finland

Institute of Pathology University Medical Center Freiburg Germany

Obstetrics and Gynecology Department Bichat Claude Bernard Hospital Paris France

Pathology Department Vall Hebron University Hospital CIBERONC Barcelona Spain

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$a Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study / $c C. Reijnen, E. Gogou, NCM. Visser, H. Engerud, J. Ramjith, LJM. van der Putten, K. van de Vijver, M. Santacana, P. Bronsert, J. Bulten, M. Hirschfeld, E. Colas, A. Gil-Moreno, A. Reques, G. Mancebo, C. Krakstad, J. Trovik, IS. Haldorsen, J. Huvila, M. Koskas, V. Weinberger, M. Bednarikova, J. Hausnerova, AAM. van der Wurff, X. Matias-Guiu, F. Amant, ENITEC Consortium, LFAG. Massuger, MPLM. Snijders, HVN. Küsters-Vandevelde, PJF. Lucas, JMA. Pijnenborg,
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$a BACKGROUND: 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.
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