Affiliation:
1. Zhongshan Hospital, Fudan University
2. Shanghai Tenth People's Hospital
3. Third People's Hospital of Honghe Hani and Yi Autonomous Prefecture
Abstract
Abstract
Background Annexal tumors containing the solid components have always been the primary focus of clinical practice and cause difficulty in differential diagnosis. Nevertheless, the existing diagnostic methods currently reported exhibit comparatively lower accuracy in identifying solid or solid-cystic ovarian diseases. This study aimed to apply the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR), the Ovarian-Adnexal Reporting and Data System (O-RADS) and contrast enhanced ultrasound (CEUS) in identical cohort of Chinese patients and to analyze their performance in discrimination of ovarian masses with solid components.
Methods This was a two-center retrospective study that included a total of 94 ovarian lesions in 86 women enrolled from January 2018 to February 2023. The lesions were classified by using the IOTA terminology and CEUS was performed for the lesions exhibiting solid components on ultrasonography, IOTA SR and O-RADS were applied, and CEUS images were analyzed retrospectively. The sensitivity, specificity, and area under the receiver-operating-characteristics curve (AUC) of the different models were also determined.
Results: Among the 94 ovarian lesions, 46 (48.9%) were benign and 48 (51.1%) were malignant. It was found that in the 60 lesions to which the SR could be applied, the sensitivity, specificity and AUC was 0.900, 0.667 and 0.783, respectively. The sensitivity, specificity and AUC of O-RADS was observed to be 1.000, 0.283 and 0.641, respectively. When SR and O-RADS were combined with CEUS, their sensitivity, specificity, and AUC values were increased to 0.917, 0.891, 0.904 and 0.958, 0.783, 0.871, respectively.
Conclusion IOTA SR and O-RADS exhibited relatively low specificity in differentiating malignant from benign ovarian lesions with the solid components, and their diagnostic performance can be significantly improved when combined with CEUS.
Publisher
Research Square Platform LLC
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