• 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 ...
    • Impact of body mass index and fat distribution on sex steroid levels in endometrial carcinoma: A retrospective study 

      van Weelden, Willem Jan; Fasmer, Kristine Eldevik; Tangen, Ingvild Løberg; IntHout, Joanna; Abbink, Karin; van Herwaarden, Antonius E.; Krakstad, Camilla; Massuger, Leon F.A.G.; Haldorsen, Ingfrid S.; Pijnenborg, Johanna M.A. (Peer reviewed; Journal article, 2019-06-07)
      Background Obesity is an important cause of multiple cancer types, amongst which endometrial cancer (EC). The relation between obesity and cancer is complicated and involves alterations in insulin metabolism, response to ...
    • Poor outcome in hypoxic endometrial carcinoma is related to vascular density 

      Reijnen, Casper; van Weelden, Willem Jan; Arts, Martijn S.J.P.; Peters, Johan P.; Rijken, Paul F.; 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; Minar, Lubos; Jandakova, Eva; Snijders, Marc P.L.M.; van den Berg-van Erp, Saskia; Küsters-Vandevelde, Heidi V.N.; Matias-Guiu, Xavier; Amant, Frederic; Massuger, Leon F.A.G.; Bussink, Johan; Pijnenborg, Johanna M.A. (Peer reviewed; Journal article, 2019-04-23)
      Background Identification of endometrial carcinoma (EC) patients at high risk of recurrence is lacking. In this study, the prognostic role of hypoxia and angiogenesis was investigated in EC patients. Methods Tumour slides ...
    • 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 ...