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

Lombaers, Marike S.; Reijnen, Casper; Sprik, Ally; Bretová, Petra; Grube, Marcel; Vrede, Stephanie; Berg, Hege F.; Asberger, Jasmin; Colas, Eva; Hausnerova, Jitka; Huvila, Jutta; Gil-Moreno, Antonio; Matias-Guiu, Xavier; Simons, Michiel; Snijders, Marc P. L. M.; Visser, Nicole C. M.; Kommoss, Stefan; Weinberger, Vit; Amant, Frederic; Bronsert, Peter; Haldorsen, Ingfrid S.; Koskas, Martin; Krakstad, Camilla; Küsters-Vandevelde, Heidi V. N.; Mancebo, Gemma; van der Putten, Louis J. M.; de la Calle; Irene; Lucas, Peter J. F.; Hommersom, Arjen; Pijnenborg, Johanna M. A.
ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment

Lombaers, Marike S.
Reijnen, Casper
Sprik, Ally
Bretová, Petra
Grube, Marcel
Vrede, Stephanie
Berg, Hege F.
Asberger, Jasmin
Colas, Eva
Hausnerova, Jitka
Huvila, Jutta
Gil-Moreno, Antonio
Matias-Guiu, Xavier
Simons, Michiel
Snijders, Marc P. L. M.
Visser, Nicole C. M.
Kommoss, Stefan
Weinberger, Vit
Amant, Frederic
Bronsert, Peter
Haldorsen, Ingfrid S.
Koskas, Martin
Krakstad, Camilla
Küsters-Vandevelde, Heidi V. N.
Mancebo, Gemma
van der Putten, Louis J. M.
de la Calle
Irene
Lucas, Peter J. F.
Hommersom, Arjen
Pijnenborg, Johanna M. A.
Katso/Avaa
Lataukset: 

Pergamon Press
doi:10.1016/j.ejca.2025.116058
URI
https://doi.org/10.1016/j.ejca.2025.116058
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe202601217208
Tiivistelmä

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.

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