Martin Koskas1, Marie Fournier2, Anke Vanderstraeten3, Francine Walker4, Dirk Timmerman5, Ignace Vergote3, Frédéric Amant6. 1. Gynecologic Oncology, Bichat University Hospital, Paris Diderot University, Paris, France; EA7285, UVSQ, Montigny-le-Bretonneux, France; Gynecologic Oncology, University Hospitals Leuven, and Department of Oncology, KU Leuven, Leuven, Belgium. Electronic address: martin.koskas@wanadoo.fr. 2. Gynecologic Oncology, Bichat University Hospital, Paris Diderot University, Paris, France. 3. Gynecologic Oncology, University Hospitals Leuven, and Department of Oncology, KU Leuven, Leuven, Belgium. 4. Department of Pathology, Bichat University Hospital, Paris Diderot University, Paris, France. 5. Department of Development and Regeneration, University Hospitals Leuven, and Department of Oncology, KU Leuven, Leuven, Belgium. 6. Gynecologic Oncology, University Hospitals Leuven, and Department of Oncology, KU Leuven, Leuven, Belgium; Centre for Gynecologic Oncology Amsterdam (CGOA), Antoni Van Leeuwenhoek-Netherlands Cancer Institute, Amsterdam, The Netherlands.
Abstract
BACKGROUND: Several models (preoperative and postoperative) have been developed to predict lymph node metastasis (LNM) in patients with endometrial cancer. The purpose of our investigation was to compare available models in a multicentre study. METHODS: In a cohort of 519 patients with endometrial cancer who had undergone primary hysterectomy and at least a pelvic lymphadenectomy, we compared the areas under the receiver-operating characteristic curves (AUCs), calibrations, rates of false negatives (FN), and the number of patients at low-risk for LNM using ten different models (three preoperative and seven postoperative). RESULTS: In all, 17.5% of patients among the study population (91 in 519) had LNM. Only one of the three preoperative models and three of the seven postoperative models had an AUC >0.75. Six models were well calibrated. Eight models yielded an FN rate of <5%. Six models could assign more than a third of patients to the low-risk group. One postoperative (a French nomogram) and one preoperative (the Korean Gynecologic Oncology Group [KGOG]) model had an AUC >0.75, to yield an FN rate of <5%, and could assign more than a third of patients to the low-risk group. CONCLUSIONS: This study supports the use of the KGOG model to decide upon lymphadenectomy preoperatively in patients with endometrial cancer. For patients who did not have lymphadenectomy, a French nomogram could be applied using pathological characteristics to decide on a secondary lymphadenectomy.
BACKGROUND: Several models (preoperative and postoperative) have been developed to predict lymph node metastasis (LNM) in patients with endometrial cancer. The purpose of our investigation was to compare available models in a multicentre study. METHODS: In a cohort of 519 patients with endometrial cancer who had undergone primary hysterectomy and at least a pelvic lymphadenectomy, we compared the areas under the receiver-operating characteristic curves (AUCs), calibrations, rates of false negatives (FN), and the number of patients at low-risk for LNM using ten different models (three preoperative and seven postoperative). RESULTS: In all, 17.5% of patients among the study population (91 in 519) had LNM. Only one of the three preoperative models and three of the seven postoperative models had an AUC >0.75. Six models were well calibrated. Eight models yielded an FN rate of <5%. Six models could assign more than a third of patients to the low-risk group. One postoperative (a French nomogram) and one preoperative (the Korean Gynecologic Oncology Group [KGOG]) model had an AUC >0.75, to yield an FN rate of <5%, and could assign more than a third of patients to the low-risk group. CONCLUSIONS: This study supports the use of the KGOG model to decide upon lymphadenectomy preoperatively in patients with endometrial cancer. For patients who did not have lymphadenectomy, a French nomogram could be applied using pathological characteristics to decide on a secondary lymphadenectomy.
Authors: Casper Reijnen; Joanna IntHout; Leon F A G Massuger; Fleur Strobbe; Heidi V N Küsters-Vandevelde; Ingfrid S Haldorsen; Marc P L M Snijders; Johanna M A Pijnenborg Journal: Oncologist Date: 2019-06-11
Authors: 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; Leon F A G Massuger; Marc P L M Snijders; Heidi V N Küsters-Vandevelde; Peter J F Lucas; Johanna M A Pijnenborg Journal: PLoS Med Date: 2020-05-15 Impact factor: 11.069
Authors: Areege Kamal; Anthony Valentijn; Roger Barraclough; Philip Rudland; Nihad Rahmatalla; Pierre Martin-Hirsch; Helen Stringfellow; Shandya B Decruze; Dharani K Hapangama Journal: Oncotarget Date: 2018-07-31