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

J 2020

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

REIJNEN, C., E. GOGOU, N. C. M. VISSER, H. ENGERUD, J. RAMJITH et. al.

Basic information

Original name

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

Authors

REIJNEN, C. (guarantor), E. GOGOU, N. C. M. VISSER, H. ENGERUD, J. RAMJITH, L. J. M. VAN DER PUTTEN, K. V. DE VIJVER, M. SANTACANA, P. BRONSERT, J. BULTEN, M. HIRSCHFELD, E. COLAS, A. GIL-MORENO, A. REQUES, G. MANCEBO, C. KRAKSTAD, J. TROVIK, I. S. HALDORSEN, J. HUVILA, M. KOSKAS, Vít WEINBERGER (203 Czech Republic, belonging to the institution), Markéta BEDNAŘÍKOVÁ (203 Czech Republic, belonging to the institution), Jitka HAUSNEROVÁ (203 Czech Republic, belonging to the institution), A. A. M. VAN DER WURFF, X. MATIAS-GUIU, F. AMANT, L. F. A. G. MASSUGER, M. P. L. M. SNIJDERS, H. V. N. KUSTERS-VANDEVELDE, P. J. F. LUCAS and J. M. A. PIJNENBORG

Edition

PLOS MEDICINE, SAN FRANCISCO, PUBLIC LIBRARY SCIENCE, 2020, 1549-1277

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30218 General and internal medicine

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 11.069

RIV identification code

RIV/00216224:14110/20:00116535

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1371/journal.pmed.1003111

UT WoS

000558077100011

Keywords in English

endometrial cancer; Bayesian network model

Abstract

V originále

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 (ENI-TEC), 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.

Citovat

REIJNEN, C., E. GOGOU, N. C. M. VISSER, H. ENGERUD, J. RAMJITH, L. J. M. VAN DER PUTTEN, K. V. DE VIJVER, M. SANTACANA, P. BRONSERT, J. BULTEN, M. HIRSCHFELD, E. COLAS, A. GIL-MORENO, A. REQUES, G. MANCEBO, C. KRAKSTAD, J. TROVIK, I. S. HALDORSEN, J. HUVILA, M. KOSKAS, Vít WEINBERGER, Markéta BEDNAŘÍKOVÁ, Jitka HAUSNEROVÁ, A. A. M. VAN DER WURFF, X. MATIAS-GUIU, F. AMANT, L. F. A. G. MASSUGER, M. P. L. M. SNIJDERS, H. V. N. KUSTERS-VANDEVELDE, P. J. F. LUCAS and J. M. A. PIJNENBORG. Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study. PLOS MEDICINE. SAN FRANCISCO: PUBLIC LIBRARY SCIENCE, 2020, vol. 17, No 5, p. 1-19. ISSN 1549-1277. Available from: https://dx.doi.org/10.1371/journal.pmed.1003111.

@article{1681896,
   author = {Reijnen, C. and Gogou, E. and Visser, N. C. M. and Engerud, H. and Ramjith, J. and van der Putten, L. J. M. and de Vijver, K. V. and Santacana, M. and Bronsert, P. and Bulten, J. and Hirschfeld, M. and Colas, E. and GilandMoreno, A. and Reques, A. and Mancebo, G. and Krakstad, C. and Trovik, J. and Haldorsen, I. S. and Huvila, J. and Koskas, M. and Weinberger, Vít and Bednaříková, Markéta and Hausnerová, Jitka and van der Wurff, A. A. M. and MatiasandGuiu, X. and Amant, F. and Massuger, L. F. A. G. and Snijders, M. P. L. M. and KustersandVandevelde, H. V. N. and Lucas, P. J. F. and Pijnenborg, J. M. A.},
   article_location = {SAN FRANCISCO},
   article_number = {5},
   doi = {http://dx.doi.org/10.1371/journal.pmed.1003111},
   keywords = {endometrial cancer; Bayesian network model},
   language = {eng},
   issn = {1549-1277},
   journal = {PLOS MEDICINE},
   title = {Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study},
   url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003111},
   volume = {17},
   year = {2020}
}

TY  - JOUR
ID  - 1681896
AU  - Reijnen, C. - Gogou, E. - Visser, N. C. M. - Engerud, H. - Ramjith, J. - van der Putten, L. J. M. - de Vijver, K. V. - Santacana, M. - Bronsert, P. - Bulten, J. - Hirschfeld, M. - Colas, E. - Gil-Moreno, A. - Reques, A. - Mancebo, G. - Krakstad, C. - Trovik, J. - Haldorsen, I. S. - Huvila, J. - Koskas, M. - Weinberger, Vít - Bednaříková, Markéta - Hausnerová, Jitka - van der Wurff, A. A. M. - Matias-Guiu, X. - Amant, F. - Massuger, L. F. A. G. - Snijders, M. P. L. M. - Kusters-Vandevelde, H. V. N. - Lucas, P. J. F. - Pijnenborg, J. M. A.
PY  - 2020
TI  - Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study
JF  - PLOS MEDICINE
VL  - 17
IS  - 5
SP  - 1-19
EP  - 1-19
PB  - PUBLIC LIBRARY SCIENCE
SN  - 15491277
KW  - endometrial cancer
KW  - Bayesian network model
UR  - https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003111
L2  - https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003111
N2  - 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 (ENI-TEC), 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.
ER  -

REIJNEN, C., E. GOGOU, N. C. M. VISSER, H. ENGERUD, J. RAMJITH, L. J. M. VAN DER PUTTEN, K. V. DE VIJVER, M. SANTACANA, P. BRONSERT, J. BULTEN, M. HIRSCHFELD, E. COLAS, A. GIL-MORENO, A. REQUES, G. MANCEBO, C. KRAKSTAD, J. TROVIK, I. S. HALDORSEN, J. HUVILA, M. KOSKAS, Vít WEINBERGER, Markéta BEDNAŘÍKOVÁ, Jitka HAUSNEROVÁ, A. A. M. VAN DER WURFF, X. MATIAS-GUIU, F. AMANT, L. F. A. G. MASSUGER, M. P. L. M. SNIJDERS, H. V. N. KUSTERS-VANDEVELDE, P. J. F. LUCAS and J. M. A. PIJNENBORG. Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study. \textit{PLOS MEDICINE}. SAN FRANCISCO: PUBLIC LIBRARY SCIENCE, 2020, vol.~17, No~5, p.~1-19. ISSN~1549-1277. Available from: https://dx.doi.org/10.1371/journal.pmed.1003111.

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