Literature DB >> 34107646

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumours.

D Timmerman, F Planchamp, T Bourne, C Landolfo, A du Bois, L Chiva, D Cibula, N Concin, D Fischerova, W Froyman, G Gallardo, B Lemley, A Loft, L Mereu, P Morice, D Querleu, C Testa, I Vergote, V Vandecaveye, G Scambia, C Fotopoulou.   

Abstract

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the preoperative diagnosis of ovarian tumours, including imaging techniques, biomarkers and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumours and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumours and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

Entities:  

Year:  2021        PMID: 34107646     DOI: 10.52054/FVVO.13.2.016

Source DB:  PubMed          Journal:  Facts Views Vis Obgyn        ISSN: 2032-0418


  1 in total

1.  Defining Models to Classify between Benign and Malignant Adnexal Masses Using Routine Laboratory Parameters.

Authors:  Elisabeth Reiser; Dietmar Pils; Christoph Grimm; Ines Hoffmann; Stephan Polterauer; Marlene Kranawetter; Stefanie Aust
Journal:  Cancers (Basel)       Date:  2022-06-30       Impact factor: 6.575

  1 in total

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