abstract: Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis (IOTA) group
BACKGROUND:
Accurate
methods to preoperatively characterize adnexal tumors are pivotal for
optimal patient management. A recent meta-analysis concluded that the
International Ovarian Tumor Analysis (IOTA) algorithms such as the
Simple Rules are the best approaches to preoperatively classify adnexal
masses as benign or malignant.
OBJECTIVE:
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:
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 three phases between 2002 and 2012.
The five Simple Rules features indicative of a benign tumor (B-features)
and the five 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 ten ultrasound features and type of center. The diagnostic
performance was evaluated by area under the receiver operating
characteristic curve (AUC), sensitivity, specificity, positive and
negative likelihood ratios (LR+, LR-), positive and negative predictive
values (PPV, 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 AUC on
validation data was very similar in oncology centers (0.917, 95% CI
0.901 to 0.931) and other centers (0.916, 95% CI 0.873 to 0.945). Risk
estimates showed good calibration. 23% of patients in the validation
data set had a very low estimated risk (<1%), 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.
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