RESUMEN
BACKGROUND: Mathematical predictive models for ovarian tumors have an advantage over subjective assessment due to their relative simplicity, and therefore usefulness for less experienced sonographers. It is currently unclear which predictive model is best at predicting the nature of an ovarian tumor. PURPOSE: To compare the diagnostic predictive accuracy of the International Ovarian Tumour Analysis Simple Rules (IOTA SR) with Risk of Malignancy Index (RMI), to differentiate between benign and malignant ovarian tumors. MATERIAL AND METHODS: A total of 202 women diagnosed with ovarian tumor(s) were included. Preoperatively, patients were examined through transvaginal ultrasonography and CA-125 (U/mL) levels were measured. RMI and IOTA SR were determined, and where possible compared to definitive histopathological diagnosis. RESULTS: Of the 202 women with ovarian tumors, 168 women were included in this cohort study. Of these tumors, 118 (70.2%) were benign, 17 (10.1%) were borderline, and 33 (19.7%) were malignant. The sensitivity, specificity, and area under the curve for the RMI were 72.0%, 90.7%, and 0.896, respectively. For the IOTA SR, these were 90.0%, 68.6%, and 0.793, respectively. CONCLUSION: This cohort study shows that the RMI is a relatively useful diagnostic model in characterizing ovarian tumors, compared to the IOTA SR. However, due to the relatively low sensitivity of the RMI and high rate of inconclusive results of the IOTA SR, both diagnostic tests do not seem discriminative enough. Therefore, alternative diagnostic models are necessary.