Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study
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 me...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10459.1/83394 |
| Acceso en línea: | https://doi.org/10.1371/journal.pmed.1003111 http://hdl.handle.net/10459.1/83394 |
| Access Level: | acceso abierto |
| Palabra clave: | Endometrial cancer Preoperative risk Bayesian networks |
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Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study |
| title |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study |
| spellingShingle |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study Reijnen, Casper Endometrial cancer Preoperative risk Bayesian networks |
| title_short |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study |
| title_full |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study |
| title_fullStr |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study |
| title_full_unstemmed |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study |
| title_sort |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study |
| dc.creator.none.fl_str_mv |
Reijnen, Casper Gogou, Evangelia Visser, Nicole C. M. Engerud, Hilde Ramjith, Jordache van der Putten, Louis J. M. van de Vijver, Koen Santacana Espasa, Maria Bronsert, Peter Bulten, Johan Hirschfeld, Marc Colás, Eva Gil-Moreno, Antonio Reques, Armando Mancebo, Gemma Krakstad, Camilla Trovik, Jone Haldorsen, Ingfrid S. Huvila, Jutta Koskas, Martin Weinberger, Vit Bednaříková, Markéta Hausnerova, Jitka van der Wurff, Anneke A. M. Matias-Guiu, Xavier Amant, Frederic Massuger, Leon F. A. G. Snijders, Marc P. L. M. Küsters-Vandevelde, Heidi V. N Lucas, Peter J. F. Pijnenborg, Johanna M. A. |
| author |
Reijnen, Casper |
| author_facet |
Reijnen, Casper Gogou, Evangelia Visser, Nicole C. M. Engerud, Hilde Ramjith, Jordache van der Putten, Louis J. M. van de Vijver, Koen Santacana Espasa, Maria Bronsert, Peter Bulten, Johan Hirschfeld, Marc Colás, Eva Gil-Moreno, Antonio Reques, Armando Mancebo, Gemma Krakstad, Camilla Trovik, Jone Haldorsen, Ingfrid S. Huvila, Jutta Koskas, Martin Weinberger, Vit Bednaříková, Markéta Hausnerova, Jitka van der Wurff, Anneke A. M. Matias-Guiu, Xavier Amant, Frederic Massuger, Leon F. A. G. Snijders, Marc P. L. M. Küsters-Vandevelde, Heidi V. N Lucas, Peter J. F. Pijnenborg, Johanna M. A. |
| author_role |
author |
| author2 |
Gogou, Evangelia Visser, Nicole C. M. Engerud, Hilde Ramjith, Jordache van der Putten, Louis J. M. van de Vijver, Koen Santacana Espasa, Maria Bronsert, Peter Bulten, Johan Hirschfeld, Marc Colás, Eva Gil-Moreno, Antonio Reques, Armando Mancebo, Gemma Krakstad, Camilla Trovik, Jone Haldorsen, Ingfrid S. Huvila, Jutta Koskas, Martin Weinberger, Vit Bednaříková, Markéta Hausnerova, Jitka van der Wurff, Anneke A. M. Matias-Guiu, Xavier Amant, Frederic Massuger, Leon F. A. G. Snijders, Marc P. L. M. Küsters-Vandevelde, Heidi V. N Lucas, Peter J. F. Pijnenborg, Johanna M. A. |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Endometrial cancer Preoperative risk Bayesian networks |
| topic |
Endometrial cancer Preoperative risk Bayesian networks |
| description |
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. |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://doi.org/10.1371/journal.pmed.1003111 http://hdl.handle.net/10459.1/83394 |
| url |
https://doi.org/10.1371/journal.pmed.1003111 http://hdl.handle.net/10459.1/83394 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Reproducció del document publicat a https://doi.org/10.1371/journal.pmed.1003111 Plos Medicine, 2020, vol. 17, núm. 5, e1003111 |
| dc.rights.none.fl_str_mv |
cc-by (c) Reijnen et al., 2020 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
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cc-by (c) Reijnen et al., 2020 http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
| dc.publisher.none.fl_str_mv |
Public Library of Science |
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Public Library of Science |
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reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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1869418950517850112 |
| spelling |
Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation studyReijnen, CasperGogou, EvangeliaVisser, Nicole C. M.Engerud, HildeRamjith, Jordachevan der Putten, Louis J. M.van de Vijver, KoenSantacana Espasa, MariaBronsert, PeterBulten, JohanHirschfeld, MarcColás, EvaGil-Moreno, AntonioReques, ArmandoMancebo, GemmaKrakstad, CamillaTrovik, JoneHaldorsen, Ingfrid S.Huvila, JuttaKoskas, MartinWeinberger, VitBednaříková, MarkétaHausnerova, Jitkavan der Wurff, Anneke A. M.Matias-Guiu, XavierAmant, FredericMassuger, Leon F. A. G.Snijders, Marc P. L. M.Küsters-Vandevelde, Heidi V. NLucas, Peter J. F.Pijnenborg, Johanna M. A.Endometrial cancerPreoperative riskBayesian networksBackground 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.This work was supported by the Dutch Cancer Society (JMAP, Grant: 10616/2016-2). The funder did not play any role in the design and conduct of the study; in the collection, management, analysis, or interpretation of the data; or in the preparation, review, or approval of the manuscript.Public Library of Science2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1371/journal.pmed.1003111http://hdl.handle.net/10459.1/83394reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a https://doi.org/10.1371/journal.pmed.1003111Plos Medicine, 2020, vol. 17, núm. 5, e1003111cc-by (c) Reijnen et al., 2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/833942026-05-29T05:05:01Z |
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15.812429 |