Development and validation of an endometrial carcinoma preoperative bayesian network using molecular and clinical biomarkers (ENDORISK): an ENITEC collaboration study. (1st November 2019)

• Record Type: Journal Article
• Authors: Reijnen, C; Gogou, E; van der Putten, L; Visser, N; van de Vijver, K; Santacana, M; Bulten, J; Colas, E; Gil-Moreno, A; Reques, A; Mancebo, G; Krakstad, C; Trovik, J; Haldorsen, I; Engerud, H; Huvila, J; Koskas, M; Weinberger, V; Minar, L; Jandakova, E; van der Wurff, A; Matias-Guiu, X; Amant, F; Küsters-Vandevelde, H; Ramjith, J; Massuger, L; Snijders, M; Lucas, P; Pijnenborg, J
• Is Part Of: International journal of gynecological cancer. Volume 29(2019)Supplement 4
• Page Start: A6
• Page End: A7
• Publication Date: 2019-11-01
• Subjects: Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1136/ijgc-2019-ESGO.7
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Introduction/Background: The presence of pelvic and/or para-aortic lymph node metastasis (LNM) is one of the most important prognostic factors for poor outcome in endometrial carcinoma (EC). Current risk stratification for lymphadenectomy is mainly based on preoperative tumor grade, results in over- and undertreated of approximately 25% and 15% of the patients. Use of preoperative prediction models allow a personalized risk estimation and contribute to shared decision making, balancing risks and clinical benefit in tailored treatment. The aim of this study is to develop a Bayesian network (BN), based on easily-accessible clinical, histopathological and molecular biomarkers, for the prediction of lymph node metastasis and outcome in endometrial carcinoma patients. Second, the calibration and discrimination this network will be tested by means of external validation. Methodology: This network was constructed within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), using a cohort including 809 patients treated for EC. The network was based both on expert knowledge of EC progression and learned from data of the construction cohort. Variables used to construct to BN included: preoperative tumor grade; immunohistochemical profile including estrogen receptor-, progesterone receptor-, p53- and L1CAM-expression; cancer antigen 125 serum levels, thrombocyte count, imaging results and cervical cytology. Internal cross-validation and external validation was performed using two independent validation cohorts comprising 431 and 400 patients. Results: A Bayesian network was constructed to predict the presence of lymph node metastasis and 1-, 3- and 5-year disease-specific survival (figure 1 ). Internal cross-validation showed good discrimination (area under the receiver operator characteristic curve 0.86) and was calibrated well with respect to the prediction of lymph node metastasis. External validation will be completed soon. Conclusion: We have developed and externally validated a Bayesian network predicting lymph node metastasis in endometrial carcinoma using preoperative markers with high diagnostic accuracy. Disclosure: Nothing to disclose.