title: Mathematical models to discriminate between benign and malignant adnexal masses: potential diagnostic improvement using ovarian HistoScanning.
abstract: OBJECTIVE
  Accurate preoperative clinical assessment of adnexal masses can optimize outcomes by ensuring appropriate and timely surgery. This article addresses whether a new technology, ovarian HistoScanning, has an additional diagnostic value in mathematical models developed for the differential diagnosis of adnexal masses.
  
  
  METHODS
  Transvaginal sonography-based morphological variables were obtained through blinded analysis of archived images in 199 women enrolled in a prospective study to assess the performance of ovarian HistoScanning. Logistic regression (LR) and neural network (NN) models including these variables and clinical and patient data along with the HistoScanning score (HSS) (range, 0-125; based on mathematical algorithms) were developed in a learning set (60% patients). The remaining 40% patients (evaluation set) were used to assess model performance.
  
  
  RESULTS
  Of all morphological and clinical variables tested, serum CA-125, presence of a solid component, and HSS were most significant and used to develop the LR model. The NN model included all variables. The novel variable, HSS, offered significant improvement in the LR and NN models' performance. The LR and NN models in an independent evaluation set were found to have area under the receiver operating characteristic curve = 0.97 (95% confidence interval [CI], 94-99) and 0.93 (95% CI, 88-98), sensitivities = 83% (95% CI, 71%-91%) and 80% (95% CI, 67%-89%), and specificities = 98% (95% CI, 89%-99%) and 86% (95% CI, 72%-95%), respectively. In addition, these models showed an improved performance when compared with 3 other existing models (all P < 0.05).
  
  
  CONCLUSIONS
  This initial report shows a clear benefit of including ovarian HistoScanning into mathematical models used for discriminating benign from malignant ovarian masses. These models may be specifically helpful to the less experienced examiner. Future research should assess performance of these models in prospective clinical trials in different populations.
authors.affiliation: Université catholique de Louvain, Institut de Recherche Experimentale et Clinique, Epidemiology and Biostatistics Unit, Brussels, Belgium. evelien.vaes@uclouvain.be
authors.forename: Evelien
authors.initials: E
authors.lastname: Vaes
authors.forename: Ranjit
authors.initials: R
authors.lastname: Manchanda
authors.forename: Rina
authors.initials: R
authors.lastname: Nir
authors.forename: Dror
authors.initials: D
authors.lastname: Nir
authors.forename: Harry
authors.initials: H
authors.lastname: Bleiberg
authors.forename: Philippe
authors.initials: P
authors.lastname: Autier
authors.forename: Usha
authors.initials: U
authors.lastname: Menon
authors.forename: Annie
authors.initials: A
authors.lastname: Robert
mesh_terms: D000368:Aged; D018394:CA-125 Antigen / Q000097:blood; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016015:Logistic Models; D008875:Middle Aged; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D016037:Single-Blind Method; D014463:Ultrasonography
chemical_list: D018394:CA-125 Antigen