• Addition of IMP3 to L1CAM for discrimination between low- and high-grade endometrial carcinomas: a European Network for Individualised Treatment of Endometrial Cancer collaboration study 

      Visser, Nicole C.M.; van der Putten, Louis J.M.; van Egerschot, Alex; van de Vijver, Koen K.; Santacana, Maria; Bronsert, Peter; Hirschfeld, Marc; Colas, Eva; Gil-Moreno, Antonio; Garcia, Angel; Mancebo, Gemma; Alameda, Francesc; Krakstad, Camilla; Tangen, Ingvild Løberg; Huvila, Jutta; Schrauwen, Stefanie; Koskas, Martin; Walker, Francine; Weinberger, Vit; Minar, Lubos; Hausnerova, Jitka; Snijders, Marc P.L.M.; van den Berg-van Erp, Saskia; Matias-Guiu, Xavier; Trovik, Jone; Amant, Frédéric; Massuger, Leon F.A.G.; Bulten, Johan; Pijnenborg, Johanna M.A. (Peer reviewed; Journal article, 2019)
      Discrimination between low- and high-grade endometrial carcinomas (ECs) is clinically relevant but can be challenging for pathologists, with moderate interobserver agreement. Insulin-like growth factor-II mRNA-binding ...
    • Expression of L1CAM in curettage or high L1CAM level in preoperative blood samples predicts lymph node metastases and poor outcome in endometrial cancer patients 

      Tangen, Ingvild Løberg; Kopperud, Reidun Kristin; Visser, Nicole C.M.; Staff, Anne Cathrine; Tingulstad, Solveig; Marcickiewicz, Janusz; Amant, Frédéric; Bjørge, Line; Pijnenborg, Johanna M.A.; Salvesen, Helga; Werner, Henrica Maria Johanna; Trovik, Jone; Krakstad, Camilla (Peer reviewed; Journal article, 2017-09)
      Background: Several studies have identified L1 cell adhesion molecule (L1CAM) as a strong prognostic marker in endometrial cancer. To further underline the clinical usefulness of this biomarker, we investigated L1CAM as a ...
    • Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study 

      Reijnen, Casper; Gogou, Evangelia; Visser, Nicole C.M.; Engerud, Hilde; Ramjith, Jordache; Van Der Putten, Louis J.M.; Van De Vijver, Koen; Santacana, Maria; Bronsert, Peter; Bulten, Johan; Hirschfeld, Marc; Colas, Eva; Gil-Moreno, Antonio; Reques, Armando; Mancebo, Gemma; Krakstad, Camilla; Trovik, Jone; Haldorsen, Ingfrid S.; Huvila, Jutta; Koskas, Martin; Weinberger, Vit; Bednarikova, Marketa; Hausnerova, Jitka; Van Der Wurff, Anneke A. M.; Matias-Guiu, Xavier; Amant, Frédéric; Massuger, Leon F.A.G.; Snijders, Marc P.L.M.; Küsters-Vandevelde, Heidi V.N.; Lucas, Peter J. F.; Pijnenborg, Johanna M.A. (Journal article; Peer reviewed, 2020)
      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 ...