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Original Paper
Ovarian cancer prediction in adnexal masses using ultrasound-based logistic regression models: a temporal and external validation study by the IOTA group
Article first published online: 30 MAR 2010
DOI: 10.1002/uog.7636
Copyright © 2010 ISUOG. Published by John Wiley & Sons, Ltd.
Additional Information
How to Cite
Timmerman, D., Van Calster, B., Testa, A. C., Guerriero, S., Fischerova, D., Lissoni, A. A., Van Holsbeke, C., Fruscio, R., Czekierdowski, A., Jurkovic, D., Savelli, L., Vergote, I., Bourne, T., Van Huffel, S. and Valentin, L. (2010), Ovarian cancer prediction in adnexal masses using ultrasound-based logistic regression models: a temporal and external validation study by the IOTA group. Ultrasound Obstet Gynecol, 36: 226–234. doi: 10.1002/uog.7636
Publication History
- Issue published online: 28 JUL 2010
- Article first published online: 30 MAR 2010
- Manuscript Accepted: 3 MAR 2010
Funded by
- Swedish Medical Research Council. Grant Numbers: K2001-72X 11605-06A, K2002-72X-11605-07B, K2004-73X-11605-09A, K2006-73X-11605-11-3
- Research Foundation—Flanders (FWO), Belgium
Keywords:
- color Doppler ultrasonography;
- logistic models;
- ovarian neoplasms;
- sensitivity and specificity;
- ultrasonography
Abstract
Objectives
The aims of the study were to temporally and externally validate the diagnostic performance of two logistic regression models containing clinical and ultrasound variables in order to estimate the risk of malignancy in adnexal masses, and to compare the results with the subjective interpretation of ultrasound findings carried out by an experienced ultrasound examiner (‘subjective assessment’).
Methods
Patients with adnexal masses, who were put forward by the 19 centers participating in the study, underwent a standardized transvaginal ultrasound examination by a gynecologist or a radiologist specialized in ultrasonography. The examiner prospectively collected information on clinical and ultrasound variables, and classified each mass as benign or malignant on the basis of subjective evaluation of ultrasound findings. The gold standard was the histology of the mass with local clinicians deciding whether to operate on the basis of ultrasound results and the clinical picture. The models' ability to discriminate between malignant and benign masses was assessed, together with the accuracy of the risk estimates.
Results
Of the 1938 patients included in the study, 1396 had benign, 373 had primary invasive, 111 had borderline malignant and 58 had metastatic tumors. On external validation (997 patients from 12 centers), the area under the receiver–operating characteristics curve (AUC) for a model containing 12 predictors (LR1) was 0.956, for a reduced model with six predictors (LR2) was 0.949 and for subjective assessment was 0.949. Subjective assessment gave a positive likelihood ratio of 11.0 and a negative likelihood ratio of 0.14. The corresponding likelihood ratios for a previously derived probability threshold (0.1) were 6.84 and 0.09 for LR1, and 6.36 and 0.10 for LR2. On temporal validation (941 patients from seven centers), the AUCs were 0.945 (LR1), 0.918 (LR2) and 0.959 (subjective assessment).
Conclusions
Both models provide excellent discrimination between benign and malignant masses. Because the models provide an objective and reasonably accurate risk estimation, they may improve the management of women with suspected ovarian pathology. Copyright © 2010 ISUOG. Published by John Wiley & Sons, Ltd.