Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, multicentrická studie, práce podpořená grantem, validační studie
PubMed
26800772
DOI
10.1016/j.ajog.2016.01.007
PII: S0002-9378(16)00009-0
Knihovny.cz E-zdroje
- Klíčová slova
- International Ovarian Tumor Analysis, Simple Rules, adnexa, color Doppler, diagnosis, diagnostic algorithm, logistic regression analysis, ovarian cancer, ovarian neoplasms, preoperative evaluation, risk assessment, ultrasonography,
- MeSH
- hodnocení rizik MeSH
- kohortové studie MeSH
- lidé MeSH
- logistické modely MeSH
- nemoci děložních adnex diagnostické zobrazování MeSH
- nemocnice MeSH
- onkologická péče - zařízení MeSH
- prediktivní hodnota testů MeSH
- průřezové studie MeSH
- ROC křivka MeSH
- senzitivita a specificita MeSH
- ultrasonografie dopplerovská barevná MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- práce podpořená grantem MeSH
- validační studie MeSH
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.
1st Department of Gynecological Oncology and Gynecology Medical University of Lublin Lublin Poland
Clinic of Obstetrics and Gynecology University of Milan Bicocca San Gerardo Hospital Monza Italy
Department of Development and Regeneration KU Leuven Leuven Belgium
Department of Obstetrics and Gynecology Azienda Ospedaliero Universitaria di Cagliari Cagliari Italy
Department of Obstetrics and Gynecology University of Udine Udine Italy
Department of Oncology Catholic University of the Sacred Heart Rome Italy
Departments of Obstetrics and Gynecology at Karolinska University Hospital Stockholm Sweden
Preventive Gynecology Unit Division of Gynecology European Institute of Oncology Milan Italy
Skåne University Hospital Malmö Lund University Malmö Sweden
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