title: Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis.
abstract: BACKGROUND
  Many national guidelines concerning the management of ovarian cancer currently advocate the risk of malignancy index (RMI) to characterise ovarian pathology. However, other methods, such as subjective assessment, International Ovarian Tumour Analysis (IOTA) simple ultrasound-based rules (simple rules) and IOTA logistic regression model 2 (LR2) seem to be superior to the RMI. Our objective was to compare the diagnostic accuracy of subjective assessment, simple rules, LR2 and RMI for differentiating benign from malignant adnexal masses prior to surgery.
  
  
  METHODS
  MEDLINE, EMBASE and CENTRAL were searched (January 1990-August 2015). Eligibility criteria were prospective diagnostic studies designed to preoperatively predict ovarian cancer in women with an adnexal mass.
  
  
  RESULTS
  We analysed 47 articles, enrolling 19,674 adnexal tumours; 13,953 (70.9%) benign and 5721 (29.1%) malignant. Subjective assessment by experts performed best with a pooled sensitivity of 0.93 (95% confidence interval [CI] 0.92-0.95) and specificity of 0.89 (95% CI 0.86-0.92). Simple rules (classifying inconclusives as malignant) (sensitivity 0.93 [95% CI 0.91-0.95] and specificity 0.80 [95% CI 0.77-0.82]) and LR2 (sensitivity 0.93 [95% CI 0.89-0.95] and specificity 0.84 [95% CI 0.78-0.89]) outperformed RMI (sensitivity 0.75 [95% CI 0.72-0.79], specificity 0.92 [95% CI 0.88-0.94]). A two-step strategy using simple rules, when inconclusive added by subjective assessment, matched test performance of subjective assessment by expert examiners (sensitivity 0.91 [95% CI 0.89-0.93] and specificity 0.91 [95% CI 0.87-0.94]).
  
  
  CONCLUSIONS
  A two-step strategy of simple rules with subjective assessment for inconclusive tumours yielded best results and matched test performance of expert ultrasound examiners. The LR2 model can be used as an alternative if an expert is not available.
authors.affiliation: Department of Obstetrics and Gynaecology, Maastricht University Medical Centre+ (MUMC+), GROW - School for Oncology and Developmental Biology, P.Debyelaan 25, 6202 AZ, Maastricht, The Netherlands. Electronic address: evelyne.meys@mumc.nl.
authors.forename: E M J
authors.initials: EM
authors.lastname: Meys
authors.affiliation: Department of Obstetrics and Gynaecology and Leuven Cancer Institute, University Hospital KU Leuven, Herestraat 49, Leuven 3000, Belgium. Electronic address: j.kaijser@ikazia.nl.
authors.forename: J
authors.initials: J
authors.lastname: Kaijser
authors.affiliation: Department of Obstetrics and Gynaecology, Maastricht University Medical Centre+ (MUMC+), GROW - School for Oncology and Developmental Biology, P.Debyelaan 25, 6202 AZ, Maastricht, The Netherlands. Electronic address: r.kruitwagen@mumc.nl.
authors.forename: R F P M
authors.initials: RF
authors.lastname: Kruitwagen
authors.affiliation: Department of Obstetrics and Gynaecology, Maastricht University Medical Centre+ (MUMC+), GROW - School for Oncology and Developmental Biology, P.Debyelaan 25, 6202 AZ, Maastricht, The Netherlands. Electronic address: brigitte.slangen@mumc.nl.
authors.forename: B F M
authors.initials: BF
authors.lastname: Slangen
authors.affiliation: Department of Development and Regeneration, KU Leuven, Herestraat 49, Leuven 3000, Belgium. Electronic address: ben.vancalster@med.kuleuven.be.
authors.forename: B
authors.initials: B
authors.lastname: Van Calster
authors.affiliation: Department of General Practice, KU Leuven, Kapucijnenvoer 33, Leuven 3000, Belgium. Electronic address: bert.aertgeerts@med.kuleuven.be.
authors.forename: B
authors.initials: B
authors.lastname: Aertgeerts
authors.affiliation: Department of General Practice, KU Leuven, Kapucijnenvoer 33, Leuven 3000, Belgium; Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK. Electronic address: jan.verbakel@med.kuleuven.be.
authors.forename: J Y
authors.initials: JY
authors.lastname: Verbakel
authors.affiliation: Department of Obstetrics and Gynaecology and Leuven Cancer Institute, University Hospital KU Leuven, Herestraat 49, Leuven 3000, Belgium. Electronic address: dirk.timmerman@uzleuven.be.
authors.forename: D
authors.initials: D
authors.lastname: Timmerman
authors.affiliation: Department of Obstetrics and Gynaecology, Maastricht University Medical Centre+ (MUMC+), GROW - School for Oncology and Developmental Biology, P.Debyelaan 25, 6202 AZ, Maastricht, The Netherlands. Electronic address: Toon.van.gorp@mumc.nl.
authors.forename: T
authors.initials: T
authors.lastname: Van Gorp
mesh_terms: D003661:Decision Support Techniques; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging* / Q000601:surgery; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D018615:Ultrasonography, Doppler, Color
keywords: Meta-analysis; Ovarian cancer; Ovarian neoplasms; Sensitivity and specificity; Systematic review; Ultrasonography