External validation of mathematical models to distinguish between benign and malignant adnexal tumors: a multicenter study by the International Ovarian Tumor Analysis Group.
OBJECTIVE
Several scoring systems have been developed to distinguish between benign and malignant adnexal tumors. However, few of them have been externally validated in new populations. Our aim was to compare their performance on a prospectively collected large multicenter data set.


METHODS
In phase I of the International Ovarian Tumor Analysis multicenter study, patients with a persistent adnexal mass were examined with transvaginal ultrasound and color Doppler imaging. More than 50 end point variables were prospectively recorded for analysis. The outcome measure was the histologic classification of excised tissue as malignant or benign. We used the International Ovarian Tumor Analysis data to test the accuracy of previously published scoring systems. Receiver operating characteristic curves were constructed to compare the performance of the models.


RESULTS
Data from 1,066 patients were included; 800 patients (75%) had benign tumors and 266 patients (25%) had malignant tumors. The morphologic scoring system used by Lerner gave an area under the receiver operating characteristic curve (AUC) of 0.68, whereas the multimodal risk of malignancy index used by Jacobs gave an AUC of 0.88. The corresponding values for logistic regression and artificial neural network models varied between 0.76 and 0.91 and between 0.87 and 0.90, respectively. Advanced kernel-based classifiers gave an AUC of up to 0.92.


CONCLUSIONS
The performance of the risk of malignancy index was similar to that of most logistic regression and artificial neural network models. The best result was obtained with a relevance vector machine with radial basis function kernel. Because the models were tested on a large multicenter data set, results are likely to be generally applicable.
Van Holsbeke
Caroline
C
Department of Obstetrics and Gynecology, University Hospitals KU Leuven, Belgium.
Van Calster
Ben
B
Valentin
Lil
L
Testa
Antonia C
AC
Ferrazzi
Enrico
E
Dimou
Ioannis
I
Lu
Chuan
C
Moerman
Philippe
P
Van Huffel
Sabine
S
Vergote
Ignace
I
Timmerman
Dirk
D
D000291:Adnexal Diseases / Q000145:classification / Q000175:diagnosis; D018394:CA-125 Antigen; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007390:International Agencies; D016015:Logistic Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000145:classification / Q000175:diagnosis*; D011300:Preoperative Care / Q000706:statistics & numerical data*; D011446:Prospective Studies
D018394:CA-125 Antigen