Improving Ultrasound Detection of Uterine Adenomyosis Through Computational Texture Analysis

Abstract The purpose of our study was to determine if a textural analysis metric can be implemented to improve diagnosis of adenomyosis by ultrasound. We retrospectively identified 38 patients with a magnetic resonance imaging (MRI) diagnosis of uterine adenomyosis that also had a pelvic ultrasound within 6 months. We also identified 50 normal pelvic ultrasound examinations confirmed by a normal pelvic MRI within 6 months as a control group. A region of interest (ROI) was subsequently placed on the study population ultrasound image corresponding to the area of adenomyosis on MRI. An ROI was placed in the area of the junctional zone in the normal controls. The abnormal and normal ROIs were then compared against trained normal and abnormal distributions to determine the success rate, sensitivity, specificity, and negative and positive predictive values of our computer metric. The ultrasound reports performed before MRI were also reviewed to determine the radiologist correct/incorrect interpretation rate for comparison with our textural analysis metric. Using a training population of 50 normal ultrasound examinations (confirmed with a normal MRI) and 38 abnormal ultrasound examinations (MRI confirmed adenomyosis), we had an overall 75% (66/88 accurately diagnosed) success rate with a sensitivity, specificity, and negative and positive predictive values of 70%, 79%, 73%, and 76%, respectively (P < .0001). The sensitivity and false-negative rate of the initial ultrasound interpretation were 26% (10/38) and 74% (28/38), respectively.