Performance of International Ovarian Tumor Analysis (IOTA) predictive models in preoperative discrimination between benign and malignant adnexal lesions: preliminary outcomes in a Tertiary Care Hospital in Greece

Abstract

Objectives

To apply the International Ovarian Tumor Analysis (IOTA) predictive models, the logistic regression model 2 (LR2) and the IOTA Assessment of Different NEoplasias in the adneXa (ADNEX), in patients with ovarian masses and to compare their performance in preoperative discrimination between benign and malignant adnexal lesions.

Methods

This was a retrospective diagnostic accuracy study with prospectively collected data, performed between January 2019 and December 2022, in a single tertiary gynecologic oncology center in Greece. The study included women with an adnexal lesion which underwent surgery within 6 months after of using the LR2 and ADNEX protocol to assess the risk of malignancy. Correlation of the ultrasound findings with the postoperative histopathological analysis was performed. Receiver–operating characteristics (ROC) curve analysis was used to determine the diagnostic accuracy of the models to classify tumors; sensitivity and specificity were determined for each model and their performance was compared.

Results

Of the136 participants, 117 (86%) had benign ovarian masses and 19 (14%) had malignant tumors. The area under the ROC curve (AUC) of the LR2 model was 0.84 (95% CI 0.74–0.93), which was significantly higher than the AUC for ADNEX model: 0.78 (95% CI 0.67–0.89). At a cut off > 10%, the LR2 model had the highest sensitivity 89.5% (95% CI 66.9–98.7) and specificity 85.1% (95% CI 76.9–91.2) compared to ADNEX model [sensitivity 84.2% (95% CI 60.4–96.6) and specificity 71.8% (95% CI 62.7–79.7)].

Conclusions

IOTA LR2 had the highest accuracy in differentiating between benign and malignant ovarian masses. IOTA LR2 and ADNEX models were both useful tools in discriminating between benign and malignant ovarian masses.