51. Development and validation of an endometrial carcinoma preoperative bayesian network using molecular and clinical biomarkers (ENDORISK): an ENITEC collaboration study
- Author
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Vít Weinberger, Hilde Engerud, Armando Reques, Peter J. F. Lucas, Nicole C.M. Visser, E. Gogou, Jutta Huvila, Jordache Ramjith, J.M.A. Pijnenborg, L.F.A.G. Massuger, Casper Reijnen, L.J.M. van der Putten, Ingfrid S. Haldorsen, M Snijders, Maria Santacana, M Koskas, Camilla Krakstad, Antonio Gil-Moreno, Eva Colas, Eva Jandáková, Jone Trovik, K.K. Van de Vijver, Xavier Matias-Guiu, A van der Wurff, Gemma Mancebo, Heidi V.N. Küsters-Vandevelde, Lubos Minar, Frédéric Amant, and Johan Bulten
- Subjects
Oncology ,medicine.medical_specialty ,030219 obstetrics & reproductive medicine ,Women's cancers Radboud Institute for Molecular Life Sciences [Radboudumc 17] ,Receiver operating characteristic ,business.industry ,medicine.medical_treatment ,Endometrial cancer ,Estrogen receptor ,Bayesian network ,medicine.disease ,3. Good health ,Women's cancers Radboud Institute for Health Sciences [Radboudumc 17] ,03 medical and health sciences ,0302 clinical medicine ,030220 oncology & carcinogenesis ,Internal medicine ,Progesterone receptor ,Cohort ,medicine ,Carcinoma ,Lymphadenectomy ,business - 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.
- Published
- 2019