Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group

D. Timmerman, Ben Van Calster, A. Testa, L. Savelli, D. Fischerova, Wouter Froyman, L. Wynants, C. Van Holsbeke, Stephen E. Epstein, D. Franchi, Jeroen Kaijser, Artur Czekierdowski, S. Guerriero, Robert Fruscio, F.P.G. Leone, A. Rossi, Chiara Landolfo, Ignace Vergote, T. Bourne, L. Valentin

Research output: Contribution to journalArticlepeer-review


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. Study Design 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 (
Original languageEnglish
Pages (from-to)424-437
Number of pages14
JournalAmerican Journal of Obstetrics and Gynecology
Issue number4
Publication statusPublished - 2016


  • adnexa
  • color Doppler
  • diagnosis
  • diagnostic algorithm
  • International Ovarian Tumor Analysis
  • logistic regression analysis
  • ovarian cancer
  • ovarian neoplasms
  • preoperative evaluation
  • risk assessment
  • Simple Rules
  • ultrasonography
  • adnexa tumor
  • adult
  • aged
  • algorithm
  • area under the curve
  • Article
  • ascites tumor
  • benign tumor
  • cancer diagnosis
  • cancer risk
  • cohort analysis
  • confidence interval
  • controlled study
  • cross-sectional study
  • diagnostic accuracy
  • diagnostic test accuracy study
  • female
  • gold standard
  • high risk patient
  • histopathology
  • human
  • human tissue
  • low risk patient
  • major clinical study
  • malignant neoplastic disease
  • multicenter study
  • oncologist
  • patient care
  • predictive value
  • priority journal
  • prospective study
  • receiver operating characteristic
  • sensitivity and specificity
  • Simple Rules algorithm
  • single blind procedure
  • statistical analysis
  • transvaginal echography
  • tumor classification
  • tumor volume
  • uterus tumor
  • validation study
  • vascularization
  • adnexa disease
  • cancer center
  • clinical trial
  • color Doppler flowmetry
  • echography
  • hospital
  • statistical model
  • Adnexal Diseases
  • Cancer Care Facilities
  • Cohort Studies
  • Cross-Sectional Studies
  • Female
  • Hospitals
  • Humans
  • Logistic Models
  • Predictive Value of Tests
  • Risk Assessment
  • ROC Curve
  • Sensitivity and Specificity
  • Ultrasonography, Doppler, Color


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