title: Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group.
abstract: BACKGROUND
  Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant.
  
  
  OBJECTIVE
  We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules.
  
  
  METHODS
  This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves.
  
  
  RESULTS
  Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%.
  
  
  CONCLUSIONS
  Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally.
authors.affiliation: Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium. Electronic address: dirk.timmerman@uzleuven.be.
authors.forename: Dirk
authors.initials: D
authors.lastname: Timmerman
authors.affiliation: Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
authors.forename: Ben
authors.initials: B
authors.lastname: Van Calster
authors.affiliation: Department of Oncology, Catholic University of the Sacred Heart, Rome, Italy.
authors.forename: Antonia
authors.initials: A
authors.lastname: Testa
authors.affiliation: Department of Obstetrics and Gynecology, S. Orsola-Malpighi Hospital, University of Bologna, Bologna, Italy.
authors.forename: Luca
authors.initials: L
authors.lastname: Savelli
authors.affiliation: Gynecological Oncology Center, Department of Obstetrics and Gynecology, Charles University, Prague, Czech Republic.
authors.forename: Daniela
authors.initials: D
authors.lastname: Fischerova
authors.affiliation: Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium.
authors.forename: Wouter
authors.initials: W
authors.lastname: Froyman
authors.affiliation: Department of Electrical Engineering-ESAT, Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium; iMinds Medical IT Department, KU Leuven, Leuven, Belgium.
authors.forename: Laure
authors.initials: L
authors.lastname: Wynants
authors.affiliation: Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, Ziekenhuis Oost-Limburg, Genk, Belgium.
authors.forename: Caroline
authors.initials: C
authors.lastname: Van Holsbeke
authors.affiliation: Departments of Obstetrics and Gynecology at Karolinska University Hospital, Stockholm, Sweden.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Epstein
authors.affiliation: Preventive Gynecology Unit, Division of Gynecology, European Institute of Oncology, Milan, Italy.
authors.forename: Dorella
authors.initials: D
authors.lastname: Franchi
authors.affiliation: Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium; Department of Gynecology and Obstetrics, Ikazia Hospital, Rotterdam, The Netherlands.
authors.forename: Jeroen
authors.initials: J
authors.lastname: Kaijser
authors.affiliation: First Department of Gynecological Oncology and Gynecology, Medical University of Lublin, Lublin, Poland.
authors.forename: Artur
authors.initials: A
authors.lastname: Czekierdowski
authors.affiliation: Department of Obstetrics and Gynecology, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Guerriero
authors.affiliation: Clinic of Obstetrics and Gynecology, University of Milan-Bicocca, San Gerardo Hospital, Monza, Italy.
authors.forename: Robert
authors.initials: R
authors.lastname: Fruscio
authors.affiliation: Department of Obstetrics and Gynecology, Clinical Sciences Institute L. Sacco, University of Milan, Milan, Italy.
authors.forename: Francesco P G
authors.initials: FPG
authors.lastname: Leone
authors.affiliation: Department of Obstetrics and Gynecology, University of Udine, Udine, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Rossi
authors.affiliation: Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium.
authors.forename: Chiara
authors.initials: C
authors.lastname: Landolfo
authors.affiliation: Department of Oncology, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium.
authors.forename: Ignace
authors.initials: I
authors.lastname: Vergote
authors.affiliation: Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium; Queen Charlotte's and Chelsea Hospital, Imperial College, London, United Kingdom.
authors.forename: Tom
authors.initials: T
authors.lastname: Bourne
authors.affiliation: Skåne University Hospital Malmö, Lund University, Malmö, Sweden.
authors.forename: Lil
authors.initials: L
authors.lastname: Valentin
mesh_terms: D000291:Adnexal Diseases / Q000000981:diagnostic imaging*; D002173:Cancer Care Facilities; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005260:Female; D006761:Hospitals; D006801:Humans; D016015:Logistic Models; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D018615:Ultrasonography, Doppler, Color
keywords: International Ovarian Tumor Analysis; Simple Rules; adnexa; color Doppler; diagnosis; diagnostic algorithm; logistic regression analysis; ovarian cancer; ovarian neoplasms; preoperative evaluation; risk assessment; ultrasonography