Accuracy of ultrasound subjective 'pattern recognition' for the diagnosis of borderline ovarian tumors

Abstract

Objectives: To assess the value of pattern recognition for the preoperative ultrasound diagnosis of borderline ovarian tumors (BOTs). Methods: This was a prospective study of women who were referred to our regional cancer center with the diagnosis of an adnexal mass on a Level II (routine) gynecological ultrasound scan. Women with lesions of uncertain nature were referred for a Level III (expert) ultrasound scan in our tertiary center. The tumor pattern recognition method was used to differentiate between various types of ovarian tumors. Morphological features suggestive of BOTs were: unilocular cyst with a positive ovarian crescent sign and extensive papillary projections arising from the inner wall, or a cyst with a well defined multilocular nodule. The ultrasound findings were compared with the final histological diagnosis. Results: A total of 224 women with an adnexal mass of uncertain nature were referred for an expert scan, 166 (74.1%) of whom underwent surgery. In this group of women the final histological diagnoses were: 99 (60%) benign lesions, 32 (19%) invasive ovarian cancer and 35 (21%) BOTs. Using pattern recognition combining the different morphological features, a correct preoperative diagnosis of BOT was made in 24/35 (68.6%) women: area under the receiver-operating characteristics curve 0.812 (standard error 0.049; 95% CI, 0.716-0.908), sensitivity 0.69 (95% CI, 0.52-0.81), specificity 0.94 (95% CI, 0.88-0.97), positive likelihood ratio 11.3 (95% CI, 5.53-22.8) and negative likelihood ratio 0.34 (95% CI, 0.21-0.55). Conclusions: Ultrasound diagnosis of BOTs is highly specific. However, typical features are absent in one-third of cases, which are typically misdiagnosed as benign lesions. Copyright © 2007 ISUOG. Published by John Wiley & Sons, Ltd.