title: Evaluation of a method of predicting lymph node metastasis in endometrial cancer based on five pre-operative characteristics.
abstract: OBJECTIVE
  We recently developed an algorithm based on five clinical and pathological characteristics to predict lymph node (LN) metastasis in endometrial cancer. The aim of this study was to evaluate the accuracy of using this algorithm with preoperative characteristics.
  
  
  METHODS
  In this retrospective multicenter study, we evaluated the accuracy of using an algorithm to predict LN metastasis using preoperative tumor characteristics provided by endometrial sampling pathological characteristics (histological subtype and grade) and by magnetic resonance imaging (MRI) for primary site tumor extension.
  
  
  RESULTS
  In total, 181 patients were included in this study, and 14 patients had pelvic LN metastasis (7.7%). Using preoperative tumor characteristics, the algorithm showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.83 (95% confidence interval (IC95)=0.79-0.87) and was well calibrated (average error=1.9% and maximal error=8.5%). LN metastasis prediction by the algorithm using preoperative data was as accurate as that obtained using the final tumor characteristics (AUC=0.77 (CI95=0.70-0.83), average error=2.8% and maximal error=23.2%).
  
  
  CONCLUSIONS
  Our algorithm was accurate in predicting pelvic LN metastasis even with the use of preoperative tumor characteristics provided by endometrial sampling and MRI. These findings, however, should be verified in a larger database before our algorithm is implemented for widespread use.
authors.affiliation: Department of Obstetrics and Gynaecology, APHP Hôpital Bichat, Paris, France; Paris Diderot University, Paris 07, France; UMR S 938, CdR St Antoine UPMC University, Paris 06, France; EA 7285, Université Versailles Saint-Quentin, Montigny-le-Bretonneux, France. Electronic address: martin.koskas@bch.aphp.fr.
authors.forename: Martin
authors.initials: M
authors.lastname: Koskas
authors.affiliation: Department of Obstetrics and Gynaecology, CHIC, Créteil, France.
authors.forename: Anne Sophie
authors.initials: AS
authors.lastname: Genin
authors.affiliation: Department of Obstetrics and Gynaecology, CHU Reims, Reims, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Graesslin
authors.affiliation: Department of Obstetrics and Gynaecology, APHP Hôpital Lariboisiere, Paris, France.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Barranger
authors.affiliation: Department of Obstetrics and Gynaecology, CHIC, Créteil, France.
authors.forename: Bassam
authors.initials: B
authors.lastname: Haddad
authors.affiliation: UMR S 938, CdR St Antoine UPMC University, Paris 06, France; Department of Obstetrics and Gynaecology, APHP Hôpital Tenon, Paris, France.
authors.forename: Emile
authors.initials: E
authors.lastname: Darai
authors.affiliation: EA 7285, Université Versailles Saint-Quentin, Montigny-le-Bretonneux, France; Department of Surgery, Institut Curie, Paris, France.
authors.forename: Roman
authors.initials: R
authors.lastname: Rouzier
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology*; D018262:Adenocarcinoma, Clear Cell / Q000473:pathology; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D002296:Carcinosarcoma / Q000473:pathology*; D016889:Endometrial Neoplasms / Q000473:pathology*; D005260:Female; D006801:Humans; D008197:Lymph Node Excision; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D060787:Neoplasm Grading; D009361:Neoplasm Invasiveness; D010388:Pelvis; D012189:Retrospective Studies
keywords: Algorithm; Endometrial cancer; Lymph node metastasis; Preoperative data