Prospective internal validation of mathematical models to predict malignancy in adnexal masses: results from the international ovarian tumor analysis study.
OBJECTIVE
To prospectively test the mathematical models for calculation of the risk of malignancy in adnexal masses that were developed on the International Ovarian Tumor Analysis (IOTA) phase 1 data set on a new data set and to compare their performance with that of pattern recognition, our standard method.


METHODS
Three IOTA centers included 507 new patients who all underwent a transvaginal ultrasound using the standardized IOTA protocol. The outcome measure was the histologic classification of excised tissue. The diagnostic performance of 11 mathematical models that had been developed on the phase 1 data set and of pattern recognition was expressed as area under the receiver operating characteristic curve (AUC) and as sensitivity and specificity when using the cutoffs recommended in the studies where the models had been created. For pattern recognition, an AUC was made based on level of diagnostic confidence.


RESULTS
All IOTA models performed very well and quite similarly, with sensitivity and specificity ranging between 92% and 96% and 74% and 84%, respectively, and AUCs between 0.945 and 0.950. A least squares support vector machine with linear kernel and a logistic regression model had the largest AUCs. For pattern recognition, the AUC was 0.963, sensitivity was 90.2%, and specificity was 92.9%.


CONCLUSIONS
This internal validation of mathematical models to estimate the malignancy risk in adnexal tumors shows that the IOTA models had a diagnostic performance similar to that in the original data set. Pattern recognition used by an expert sonologist remains the best method, although the difference in performance between the best mathematical model is not large.
Van Holsbeke
Caroline
C
Department of Obstetrics and Gynecology, University Hospitals Leuven, K.U. Leuven, Herestraat 49, B-3000 Leuven, Belgium.
Van Calster
Ben
B
Testa
Antonia C
AC
Domali
Ekaterini
E
Lu
Chuan
C
Van Huffel
Sabine
S
Valentin
Lil
L
Timmerman
Dirk
D
D000291:Adnexal Diseases / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology*; D000328:Adult; D019540:Area Under Curve; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology*; D010363:Pattern Recognition, Automated; D011446:Prospective Studies; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D014463:Ultrasonography