Automatic Texture-Based Analysis in Ultrasound Imaging of Ovarian Masses

Abstract

To assess the diagnostic accuracy of a new automatic texture-based algorithm (ATBA) in ultrasound imaging of ovarian masses and to compare its performance to subjective assessment by examiners with different levels of ultrasound experience. A total of 105 ultrasound images from three different groups of ovarian lesions (malignancies, functional cysts, and dermoid cysts) were evaluated using ATBA and by a total of 36 examiners with four different levels of experience (9 junior trainees, 8 senior trainees, 11 senior gynecologists, and 8 experts). Cohen's κ, Youden's indices, and the sensitivity and specificity of ATBA and of each observer were calculated for every subgroup of ovarian lesions. ATBA classified 78 of the 105 masses correctly (κ = 0.62) - results that were significantly better than those of the junior and senior trainees (p = 0.02 and p < 0.01), while differences from the group of level II examiners did not reach statistical significance (p = 0.27). The best diagnostic performance (κ = 0.70) was obtained by the group of expert level III ultrasonographers. The best classification rates overall, including both ATBA and subjective assessments, were achieved in the detection of functional cysts (Youden's indices from 0.73 to 0.85), while the poorest diagnostic performance was obtained for the classification of dermoid cysts (Youden's indices from 0.28 to 0.55). ATBA showed a significantly better diagnostic performance than observers with low or medium levels of experience, emphasizing its potential value for training purposes and in providing additional diagnostic assistance for inexperienced observers.