ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment
Background ENDORISK is a Bayesian network that can assist in preoperative risk estimation of lymph node metastasis (LNM) risk in endometrial cancer (EC) with consistent performance in external validations. To reflect state of the art care, ENDORISK was optimized by integrating molecular classificati...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| 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:10459.1/469209 |
| Acceso en línea: | https://doi.org/10.1016/j.ejca.2025.116058 https://hdl.handle.net/10459.1/469209 http://hdl.handle.net/10459.1/469209 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian network Endometrial cancer Lymph node metastasis Molecular classification Myometrial invasion Risk estimation |
| id |
ES_28b3facd4be54fbd82adecbfa55fc725 |
|---|---|
| oai_identifier_str |
oai:recercat.cat:10459.1/469209 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| spelling |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessmentLombaers, MarikeReijnen, CasperSprik, AllyBretová, PetraGrube, MarcelVrede, StephanieBerg, HegeAsberger, JasminColas, EvaHausnerova, JitkaHuvila, JuttaGil Moreno, AntonioMatias-Guiu, XavierSimons, MichielSnijders, MarcVisser, NicoleKommoss, StefanWeinberger, VitAmant, FredericBronsert, PeterHaldorsen, IngfridKoskas, MartinKrakstad, CamillaKüsters Vandevelde, HeidiMancebo, Gemmavan der Putten, Louisde la Calle, IreneLucas, PeterHommersom, ArjenPijnenborg, JohannaBayesian networkEndometrial cancerLymph node metastasisMolecular classificationMyometrial invasionRisk estimationBackground ENDORISK is a Bayesian network that can assist in preoperative risk estimation of lymph node metastasis (LNM) risk in endometrial cancer (EC) with consistent performance in external validations. To reflect state of the art care, ENDORISK was optimized by integrating molecular classification and preoperative assessment of myometrial invasion (MI). Methods Variables for POLE, MSI, and preoperative assessment of MI, either by expert transvaginal ultrasound or pelvic magnetic resonance imaging (MRI), were added to develop ENDORISK-2. The p53 biomarker, part of the molecular classification, was already included in ENDORISK. External validation of ENDORISK-2 for LNM prediction was performed in two independent cohorts from: Brno (CZ), (n = 581) and Tübingen (DE), (n = 247). Findings ENDORISK-2 yielded AUCs of 0·85 (95 % CI 0·80–0·90) (CZ) and 0·86 (95 % CI 0·77–0·96) (DE) for predicting LNM. In patients with low-grade histology, 83 % (CZ) and 89 % (DE) were estimated having less than 10 % risk of LNM, with false negative rates (FNR) of 4·3 % (CZ) and 2·2 % (DE). The previously defined set of minimally required variables, i.e.: preoperative tumor grade, three of the four immunohistochemical (IHC) markers, and one clinical marker, could be interchanged with the new variables, with comparable validation metrics, including AUC values of 0·79–0·87 for LNM prediction. Interpretation. Incorporation of molecular data and preoperative MI improved the flexibility of ENDORISK with comparable diagnostic accuracy for estimating LNM as when based on low-cost immunohistochemical biomarkers. In addition, the high diagnostic accuracy in patients with low-grade EC demonstrates how ENDORISK-2 could aid clinicians in identifying patients in whom surgical lymph node assessment may safely be omitted. These results underline its power for clinical use in both high and low resource countries.Elsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1016/j.ejca.2025.116058https://hdl.handle.net/10459.1/469209http://hdl.handle.net/10459.1/469209reponame: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/10.1016/j.ejca.2025.116058European Journal of Cancer, 2025, vol. 231, 116058cc-by, (c) Marike Lombaers et al., 2025Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/4692092026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment |
| title |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment |
| spellingShingle |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment Lombaers, Marike Bayesian network Endometrial cancer Lymph node metastasis Molecular classification Myometrial invasion Risk estimation |
| title_short |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment |
| title_full |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment |
| title_fullStr |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment |
| title_full_unstemmed |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment |
| title_sort |
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment |
| dc.creator.none.fl_str_mv |
Lombaers, Marike Reijnen, Casper Sprik, Ally Bretová, Petra Grube, Marcel Vrede, Stephanie Berg, Hege Asberger, Jasmin Colas, Eva Hausnerova, Jitka Huvila, Jutta Gil Moreno, Antonio Matias-Guiu, Xavier Simons, Michiel Snijders, Marc Visser, Nicole Kommoss, Stefan Weinberger, Vit Amant, Frederic Bronsert, Peter Haldorsen, Ingfrid Koskas, Martin Krakstad, Camilla Küsters Vandevelde, Heidi Mancebo, Gemma van der Putten, Louis de la Calle, Irene Lucas, Peter Hommersom, Arjen Pijnenborg, Johanna |
| author |
Lombaers, Marike |
| author_facet |
Lombaers, Marike Reijnen, Casper Sprik, Ally Bretová, Petra Grube, Marcel Vrede, Stephanie Berg, Hege Asberger, Jasmin Colas, Eva Hausnerova, Jitka Huvila, Jutta Gil Moreno, Antonio Matias-Guiu, Xavier Simons, Michiel Snijders, Marc Visser, Nicole Kommoss, Stefan Weinberger, Vit Amant, Frederic Bronsert, Peter Haldorsen, Ingfrid Koskas, Martin Krakstad, Camilla Küsters Vandevelde, Heidi Mancebo, Gemma van der Putten, Louis de la Calle, Irene Lucas, Peter Hommersom, Arjen Pijnenborg, Johanna |
| author_role |
author |
| author2 |
Reijnen, Casper Sprik, Ally Bretová, Petra Grube, Marcel Vrede, Stephanie Berg, Hege Asberger, Jasmin Colas, Eva Hausnerova, Jitka Huvila, Jutta Gil Moreno, Antonio Matias-Guiu, Xavier Simons, Michiel Snijders, Marc Visser, Nicole Kommoss, Stefan Weinberger, Vit Amant, Frederic Bronsert, Peter Haldorsen, Ingfrid Koskas, Martin Krakstad, Camilla Küsters Vandevelde, Heidi Mancebo, Gemma van der Putten, Louis de la Calle, Irene Lucas, Peter Hommersom, Arjen Pijnenborg, Johanna |
| 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 |
| dc.subject.none.fl_str_mv |
Bayesian network Endometrial cancer Lymph node metastasis Molecular classification Myometrial invasion Risk estimation |
| topic |
Bayesian network Endometrial cancer Lymph node metastasis Molecular classification Myometrial invasion Risk estimation |
| description |
Background ENDORISK is a Bayesian network that can assist in preoperative risk estimation of lymph node metastasis (LNM) risk in endometrial cancer (EC) with consistent performance in external validations. To reflect state of the art care, ENDORISK was optimized by integrating molecular classification and preoperative assessment of myometrial invasion (MI). Methods Variables for POLE, MSI, and preoperative assessment of MI, either by expert transvaginal ultrasound or pelvic magnetic resonance imaging (MRI), were added to develop ENDORISK-2. The p53 biomarker, part of the molecular classification, was already included in ENDORISK. External validation of ENDORISK-2 for LNM prediction was performed in two independent cohorts from: Brno (CZ), (n = 581) and Tübingen (DE), (n = 247). Findings ENDORISK-2 yielded AUCs of 0·85 (95 % CI 0·80–0·90) (CZ) and 0·86 (95 % CI 0·77–0·96) (DE) for predicting LNM. In patients with low-grade histology, 83 % (CZ) and 89 % (DE) were estimated having less than 10 % risk of LNM, with false negative rates (FNR) of 4·3 % (CZ) and 2·2 % (DE). The previously defined set of minimally required variables, i.e.: preoperative tumor grade, three of the four immunohistochemical (IHC) markers, and one clinical marker, could be interchanged with the new variables, with comparable validation metrics, including AUC values of 0·79–0·87 for LNM prediction. Interpretation. Incorporation of molecular data and preoperative MI improved the flexibility of ENDORISK with comparable diagnostic accuracy for estimating LNM as when based on low-cost immunohistochemical biomarkers. In addition, the high diagnostic accuracy in patients with low-grade EC demonstrates how ENDORISK-2 could aid clinicians in identifying patients in whom surgical lymph node assessment may safely be omitted. These results underline its power for clinical use in both high and low resource countries. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://doi.org/10.1016/j.ejca.2025.116058 https://hdl.handle.net/10459.1/469209 http://hdl.handle.net/10459.1/469209 |
| url |
https://doi.org/10.1016/j.ejca.2025.116058 https://hdl.handle.net/10459.1/469209 http://hdl.handle.net/10459.1/469209 |
| 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/10.1016/j.ejca.2025.116058 European Journal of Cancer, 2025, vol. 231, 116058 |
| dc.rights.none.fl_str_mv |
cc-by, (c) Marike Lombaers et al., 2025 Attribution 4.0 International info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
| rights_invalid_str_mv |
cc-by, (c) Marike Lombaers et al., 2025 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Elsevier |
| publisher.none.fl_str_mv |
Elsevier |
| dc.source.none.fl_str_mv |
reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| instname_str |
Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| reponame_str |
Recercat. Dipósit de la Recerca de Catalunya |
| collection |
Recercat. Dipósit de la Recerca de Catalunya |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869404961539883008 |
| score |
15,812429 |