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

by Casper Reijnen, Evangelia Gogou, Nicole C. M. Visser, Hilde Engerud, Jordache Ramjith, Louis J. M. van der Putten, Koen van de Vijver, Maria Santacana, Peter Bronsert, Johan Bulten, Marc Hirschfeld, Eva Colas, Antonio Gil-Moreno, Armando Reques, Gemma Mancebo, Camilla Krakstad, Jone Trovik, Ingfrid S. Haldorsen, Jutta Huvila, Martin Koskas, Vit Weinberger, Marketa Bednarikova, Jitka Hausnerova, Anneke A. M. van der Wurff, Xavier Matias-Guiu, Frederic Amant, ENITEC Consortium , Leon F. A. G. Massuger, Marc P. L. M. Snijders, Heidi V. N. K üsters-Vandevelde, Peter J. F. Lucas, Johanna M. A. Pijnenborg BackgroundBayesian 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 findingsWithin the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58 –71), surgically treated for endometrial cancer betwe...
Source: PLoS Medicine - Category: Internal Medicine Authors: Source Type: research