Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study.

ABSTRACT: BACKGROUND:Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. METHODS AND FINDINGS:Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 yea

SUBMITTER: Reijnen C 

PROVIDER: S-EPMC7228042 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature