Performance of the IOTA ADNEX model combined with HE4 for identifying early-stage ovarian cancer - Yang, Suying; Tang, Jing; Rong, Yue; Wang, Min; Long, Jun; Chen, Cheng; Wang, Cong (Frontiers in Oncology, 2022)
Objective: This work was designed to investigate the performance of the IOTA ADNEX model combined with human epithelial protein 4 (HE4) for ovarian cancer early detection. Methods: Women accounted for 376 who were hospitalized and operated in Women and Children’s Hospital of Chongqing Medical University were selected. Ultrasonographic images, cancer antigen-125 (CA 125) levels, and HE4 levels were obtained. All cases were analyzed and the histopathological diagnosis serves as the reference standard. Based on IOTA ADNEX model post-processing software, the risk prediction value was calculated. We analyzed receiver-operating characteristics curves to determine whether the IOTA ADNEX model alone or combined with HE4 provided better diagnostic accuracy. Results: The area under the curve (AUC) of the ADNEX model alone or combined with HE4 in predicting benign and malignant ovarian tumors was 0.914 (95% CI, 0.881-0.941) and 0.916 (95% CI, 0.883-0.942), respectively. With the cut-off risk of 10%, the ADNEX model had a sensitivity of 0.93 (95% CI, 0.87-0.97) and a specificity of 0.73 (95% CI, 0.67-0.78), while combined with HE4 it had a sensitivity of 0.90 (95% CI, 0.84-0.95) and a specificity of 0.81 (95% CI, 0.76-0.86). The IOTA ADNEX model combined with HE4 was better at improving the accuracy of the differential diagnosis between different ovarian cancers (OC) than the IOTA ADNEX model alone. A significant difference was found in separating borderline masses from Stages-II–IV OC (P=0.0257). Conclusions: A combination of IOTA ADNEX model and HE4 can improve the specificity of diagnosis of ovarian benign and malignant tumors and increase the sensitivity and effectiveness of differential diagnosis of stage II-IV OC and borderline tumors.