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Logistic Regression Model to Distinguish Between the Benign and Malignant Adnexal Mass Before Surgery: A Multicenter Study by the International Ovarian Tumor Analysis Group

From the Department of Obstetrics and Gynecology, University Hospitals Katholieke Universiteit Leuven; Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium; Istituto di Clinica Ostetrica e Ginecologica, Università Cattolica del Sacro Cuore, Rome; Dipartimento di Scienze Cliniche, Sacco, Università di Milano, Milan, Italy; Department of Obstetrics and Gynaecology, St George's Hospital Medical School, University of London; King's College London, London, United Kingdom; and Department of Obstetrics and Gynecology, University Hospital Malmö, Malmö Sweden.

Address reprint requests to Dirk Timmerman, MD, PhD, Department of Obstetrics and Gynecology, University Hospitals, Katholieke Universiteit Leuven, Herestraat 49, B-3000 Leuven, Belgium; e-mail: dirk.timmerman{at}uz.kuleuven.ac.be

PURPOSE: To collect data for the development of a more universally useful logistic regression model to distinguish between a malignant and benign adnexal tumor before surgery.

PATIENTS AND METHODS: Patients had at least one persistent mass. More than 50 clinical and sonographic end points were defined and recorded for analysis. The outcome measure was the histologic classification of excised tissues as malignant or benign.

RESULTS: Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent prognostic variables for the logistic regression model were as follows: (1) personal history of ovarian cancer, (2) hormonal therapy, (3) age, (4) maximum diameter of lesion, (5) pain, (6) ascites, (7) blood flow within a solid papillary projection, (8) presence of an entirely solid tumor, (9) maximal diameter of solid component, (10) irregular internal cyst walls, (11) acoustic shadows, and (12) a color score of intratumoral blood flow. The model containing all 12 variables (M1) gave an area under the receiver operating characteristic curve of 0.95 for the development data set (n = 754 patients). The corresponding value for the test data set (n = 312 patients) was 0.94; and a probability cutoff value of .10 gave a sensitivity of 93% and a specificity of 76%.

CONCLUSION: Because the model was constructed from multicenter data, it is more likely to be generally applicable. The effectiveness of the model will be tested prospectively at different centers.

Supported by interdisciplinary research grants of the research council of the Katholieke Universiteit Leuven, Belgium (IDO/99/03 and IDO/02/09 projects), by the Belgian Programme on Interuniversity Poles of Attraction (phase V-22), by the Concerted Action Project AMBioRICS of the Flemish Community, by the FWO project G.0407.02, by the European Union Network of Excellence BIOPATTERN (contract No. FP6-2002-IST 508803), by research grants from the Swedish Medical Research Council (Grants No. K98-17X - 11,605 - 03A, K2001 - 72X - 11,605 - 06A and K2002- 72X- 11,605- 07B), by funds administered by Malmö University Hospital, Allmänna Sjukhusets i Malmö Stiftelse för bekämpande av cancer (the Malmö General Hospital Foundation for fighting against cancer), and ALF-medel (a Swedish governmental grant from the region of Scania).

Presented in part at an invited lecture at the 14th World Congress of Ultrasound in Obstetrics and Gynecology, organized by the International Society of Ultrasound in Obstetrics and Gynecology, August 31- September 4, 2004, Stockholm, Sweden.

Authors' disclosures of potential conflicts of interest are found at the end of this article.

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