A Prospective Validation of the IOTA Logistic Regression Models (LR1 and LR2) in Comparison to Subjective Pattern Recognition for the Diagnosis of Ovarian Cancer

Abstract

ObjectivesThis study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer.Methods and MaterialsThis was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared.ResultsThe AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92–0.97] and 0.93 (95% CI, 0.90–0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84–24.6) and a LR−ve of 0.049 (95% CI, 0.022–0.107). The corresponding LR+ve and LR−ve for LR1 were 3.33 (95% CI, 2.85–3.55) and 0.03 (95% CI, 0.01–0.10), and for LR2 were 3.58 (95% CI, 2.77–4.63) and 0.052 (95% CI, 0.022–0.123). The accuracy of PR was 0.942 (95% CI, 0.908–0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781–0.870) for LR1 and 0.836 (95% CI, 0.788–0.872) for LR2 (P < 0.001).ConclusionsThe AUC of the IOTA LR1 and LR2 were similar in nonexpert’s hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.