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

Antonia Carla Testa, Dirk Timmerman, Ben Van Calster, Luca Savelli, Daniela Fischerova, Wouter Froyman, Laure Wynants, Caroline Van Holsbeke, Elisabeth Epstein, Dorella Franchi, Jeroen Kaijser, Artur Czekierdowski, Stefano Guerriero, Robert Fruscio, Francesco P.G. Leone, Alberto Rossi, Chiara Landolfo, Ignace Vergote, Tom Bourne, Lil Valentin

Risultato della ricerca: Contributo in rivistaArticolo in rivista

97 Citazioni (Scopus)

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. 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 (<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%. Conclusion 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.
Lingua originaleEnglish
pagine (da-a)424-437
Numero di pagine14
RivistaAmerican Journal of Obstetrics and Gynecology
Volume214
DOI
Stato di pubblicazionePubblicato - 2016

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

Fingerprint Entra nei temi di ricerca di 'Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group'. Insieme formano una fingerprint unica.

Cita questo