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External Validation of Mathematical Models to Distinguish Between Benign and Malignant Adnexal Tumors: A Multicenter Study by the International Ovarian Tumor Analysis Group

Authors' Affiliations: Departments of ¹ Obstetrics and Gynecology, and ² Pathology, University Hospitals KU Leuven, ³ Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium; ⁴ Department of Obstetrics and Gynecology, Ziekenhuis Oost-Limburg, Genk, Belgium; ⁵ Department of Electronics and Computer Engineering, Technical University of Crete, Chania, Greece; ⁶ Department of Computer Science, University of Wales, Aberystwyth, United Kingdom; ⁷ Department of Obstetrics and Gynecology, Malmö University Hospital, Malmö, Sweden; ⁸ Istituto di Clinica Ostetrica e Ginecologica, Università Cattolica del Sacro Cuore, Rome, Italy; and ⁹ DCS Sacco, Università di Milano, Milan, Italy

Requests for reprints: Dirk Timmerman, Department of Obstetrics and Gynecology, University Hospitals, KU Leuven, Herestraat 49, B-3000 Leuven, Belgium. Phone: 32-1634-4201; Fax: 32-1634-4205; E-mail: dirk.timmerman{at}uz.kuleuven.ac.be.

Purpose: 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.

Experimental Design: 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.

Conclusion: 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.