title: The use of mathematical models to evaluate pelvic masses; can they beat an expert operator?
abstract: The pre-operative characterization of ovarian cysts remains a major challenge. Functional cysts and some other benign cysts should be managed conservatively, whereas persistent tumours may need removal. It is crucial to distinguish between malignant tumours, which are better treated by a gynaecological oncologist, and benign tumours, which may be suitable for minimal-access surgery. Over the past decade several ultrasound-based morphological scoring systems, colour Doppler parameters, logistic regression models and artificial neural networks have been proposed and tested in order to try to predict the histology of ovarian tumours. On prospective testing none of the current models can beat an expert sonologist. Signs of malignancy include the presence of papillary structures, irregular solid areas, septa and a strong vascularization at colour Doppler imaging. Further refinement of mathematical models and the results of multicentre trials need to be reviewed before the clinical use of mathematical models can be advocated.
authors.affiliation: Department of Obstetrics and Gynaecology, University Hospital Gasthuisberg, K.U.Leuven, Herestraat 49, B-3000 Leuven, Belgium. dirk.timmerman@uz.kuleuven.ac.be
authors.forename: Dirk
authors.initials: D
authors.lastname: Timmerman
mesh_terms: D014408:Biomarkers, Tumor / Q000097:blood; D018394:CA-125 Antigen / Q000097:blood; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging*; D012044:Regression Analysis; D014463:Ultrasonography
chemical_list: D014408:Biomarkers, Tumor; D018394:CA-125 Antigen