ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment

dc.contributor.authorLombaers, Marike S.
dc.contributor.authorReijnen, Casper
dc.contributor.authorSprik, Ally
dc.contributor.authorBretová, Petra
dc.contributor.authorGrube, Marcel
dc.contributor.authorVrede, Stephanie
dc.contributor.authorBerg, Hege F.
dc.contributor.authorAsberger, Jasmin
dc.contributor.authorColas, Eva
dc.contributor.authorHausnerova, Jitka
dc.contributor.authorHuvila, Jutta
dc.contributor.authorGil-Moreno, Antonio
dc.contributor.authorMatias-Guiu, Xavier
dc.contributor.authorSimons, Michiel
dc.contributor.authorSnijders, Marc P. L. M.
dc.contributor.authorVisser, Nicole C. M.
dc.contributor.authorKommoss, Stefan
dc.contributor.authorWeinberger, Vit
dc.contributor.authorAmant, Frederic
dc.contributor.authorBronsert, Peter
dc.contributor.authorHaldorsen, Ingfrid S.
dc.contributor.authorKoskas, Martin
dc.contributor.authorKrakstad, Camilla
dc.contributor.authorKüsters-Vandevelde, Heidi V. N.
dc.contributor.authorMancebo, Gemma
dc.contributor.authorvan der Putten, Louis J. M.
dc.contributor.authorde la Calle
dc.contributor.authorIrene
dc.contributor.authorLucas, Peter J. F.
dc.contributor.authorHommersom, Arjen
dc.contributor.authorPijnenborg, Johanna M. A.
dc.contributor.organizationfi=biolääketieteen laitos|en=Institute of Biomedicine|
dc.contributor.organizationfi=tyks, vsshp|en=tyks, varha|
dc.contributor.organization-code1.2.246.10.2458963.20.77952289591
dc.converis.publication-id505141979
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/505141979
dc.date.accessioned2026-01-21T12:42:47Z
dc.date.available2026-01-21T12:42:47Z
dc.description.abstract<h3>Background</h3><p>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).</p><h3>Methods</h3><p>Variables for <em>POLE</em>, 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).</p><h3>Findings</h3><p>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.</p><p><em>Interpretation.</em> 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.</p>
dc.identifier.eissn1879-0852
dc.identifier.jour-issn0959-8049
dc.identifier.olddbid212878
dc.identifier.oldhandle10024/195896
dc.identifier.urihttps://www.utupub.fi/handle/11111/53874
dc.identifier.urlhttps://doi.org/10.1016/j.ejca.2025.116058
dc.identifier.urnURN:NBN:fi-fe202601217208
dc.language.isoen
dc.okm.affiliatedauthorHuvila, Jutta
dc.okm.affiliatedauthorDataimport, tyks, vsshp
dc.okm.discipline3122 Cancersen_GB
dc.okm.discipline3122 Syöpätauditfi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherPergamon Press
dc.publisher.countryUnited Kingdomen_GB
dc.publisher.countryBritanniafi_FI
dc.publisher.country-codeGB
dc.relation.articlenumber116058
dc.relation.doi10.1016/j.ejca.2025.116058
dc.relation.ispartofjournalEuropean Journal of Cancer
dc.relation.volume231
dc.source.identifierhttps://www.utupub.fi/handle/10024/195896
dc.titleENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment
dc.year.issued2025

Tiedostot

Näytetään 1 - 1 / 1
Ladataan...
Name:
1-s2.0-S095980492500944X-main.pdf
Size:
2.4 MB
Format:
Adobe Portable Document Format