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

(2020) PLOS MEDICINE. 17(5). p.e1003111-e1003111
Author
Organization
Abstract
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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MLA
Reijnen, Casper, et al. “Preoperative Risk Stratification in Endometrial Cancer (ENDORISK) by a Bayesian Network Model : A Development and Validation Study.” PLOS MEDICINE, vol. 17, no. 5, 2020, pp. e1003111–e1003111.
APA
Reijnen, C., Gogou, E., Visser, N. C. M., Engerud, H., Ramjith, J., van der Putten, L. J. M., … Pijnenborg, J. M. A. (2020). Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model : a development and validation study. PLOS MEDICINE, 17(5), e1003111–e1003111.
Chicago author-date
Reijnen, Casper, Evangelia Gogou, Nicole C M Visser, Hilde Engerud, Jordache Ramjith, Louis J M van der Putten, Koen Van de Vijver, et al. 2020. “Preoperative Risk Stratification in Endometrial Cancer (ENDORISK) by a Bayesian Network Model : A Development and Validation Study.” PLOS MEDICINE 17 (5): e1003111–e1003111.
Chicago author-date (all authors)
Reijnen, Casper, Evangelia Gogou, Nicole C M Visser, Hilde Engerud, Jordache Ramjith, Louis J M van der Putten, Koen Van de Vijver, Maria Santacana, Peter Bronsert, Johan Bulten, Marc Hirschfeld, Eva Colas, Antonio Gil-Moreno, Armando Reques, Gemma Mancebo, Camilla Krakstad, Jone Trovik, Ingfrid S Haldorsen, Jutta Huvila, Martin Koskas, Vit Weinberger, Marketa Bednarikova, Jitka Hausnerova, Anneke A M van der Wurff, Xavier Matias-Guiu, Frederic Amant, Leon F A G Massuger, Marc P L M Snijders, Heidi V N Kusters-Vandevelde, Peter J F Lucas, and Johanna M A Pijnenborg. 2020. “Preoperative Risk Stratification in Endometrial Cancer (ENDORISK) by a Bayesian Network Model : A Development and Validation Study.” PLOS MEDICINE 17 (5): e1003111–e1003111.
Vancouver
1.
Reijnen C, Gogou E, Visser NCM, Engerud H, Ramjith J, van der Putten LJM, et al. Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model : a development and validation study. PLOS MEDICINE. 2020;17(5):e1003111–e1003111.
IEEE
[1]
C. Reijnen et al., “Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model : a development and validation study.,” PLOS MEDICINE, vol. 17, no. 5, pp. e1003111–e1003111, 2020.
@article{8662482,
  abstract     = {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.},
  author       = {Reijnen, Casper and Gogou, Evangelia and Visser, Nicole C M and Engerud, Hilde and Ramjith, Jordache and van der Putten, Louis J M and Van de Vijver, Koen and Santacana, Maria and Bronsert, Peter and Bulten, Johan and Hirschfeld, Marc and Colas, Eva and Gil-Moreno, Antonio and Reques, Armando and Mancebo, Gemma and Krakstad, Camilla and Trovik, Jone and Haldorsen, Ingfrid S and Huvila, Jutta and Koskas, Martin and Weinberger, Vit and Bednarikova, Marketa and Hausnerova, Jitka and van der Wurff, Anneke A M and Matias-Guiu, Xavier and Amant, Frederic and Massuger, Leon F A G and Snijders, Marc P L M and Kusters-Vandevelde, Heidi V N and Lucas, Peter J F and Pijnenborg, Johanna M A},
  issn         = {1549-1277},
  journal      = {PLOS MEDICINE},
  language     = {eng},
  number       = {5},
  pages        = {e1003111--e1003111},
  title        = {Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model : a development and validation study.},
  url          = {http://dx.doi.org/10.1371/journal.pmed.1003111},
  volume       = {17},
  year         = {2020},
}

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