How accurate is ultrasound pattern recognition at predicting the histological diagnosis of an ovarian mass?

Objectives: To assess the accuracy of pattern recognition for the histological diagnosis of an adnexal mass, when the examinations are performed by ultrasound experts of similar experience. Methods: Static B-mode preoperative ultrasound images, containing gray-scale and color Doppler information on the adnexal masses of 166 patients were examined independently by three expert sonologists. They all had access to relevant clinical information, but none of the experts performed the original real-time scans. The expert sonologists were asked to classify tumors into one of 11 histological groups. They were also asked to indicate the degree of confidence with which they made the diagnosis. In cases of disagreement between the experts reviewing the images, the histological diagnosis made by two of the three examiners was taken as the representative of the particular case. The gold standard was the final histology. Results: As a group the experts reached an accuracy of 83.13% in classifying the adnexal mass as benign or malignant. In six patients all three examiners gave a different histological diagnosis and these cases were excluded from further analysis. The sensitivity and specificity for the different histologies were: 91.43% (32/35) and 97.60% (122/125) for dermoid cysts; 66.67% (22/33) and 90.55% (115/127) for cystadenoma (fibroma); 93.33% (14/15) and 99.31% (144/145) for endometrioma; 68.75% (22/32) and 90.63% (116/128) for borderline ovarian tumors (BOT); 42.86% (6/14) and 95.89% (140/146) for gastrointestinal BOTs; 88.89% (16/18) and 95.77% (136/142) for serous BOTs; 88.00% (22/25) and 99.26% (134/135) for invasive epithelial cancer; and 90.00% (9/10) and 98.00% (147/150) for rare malignant tumors. Conclusions: Using pattern recognition ultrasound experts are able to make a correct histological diagnosis in nearly 80% of cases. The diagnostic accuracy was highest in cases of dermoid cysts, endometriomas, serous BOTs, invasive epithelial cancer and rare malignant tumors