External validation of Risk of Malignancy Index compared to IOTA Simple Rules

External validation of Risk of Malignancy Index compared to IOTA Simple Rules

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. To compare...

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Bibliographic Details
Published in:Acta radiologica (1987) Vol. 62; no. 5; p. 673
Main Authors: Mulder, Esmee E, Gelderblom, Malou E, Schoot, Dick, Vergeldt, Tineke Fm, Nijssen, Donna L, Piek, Jurgen Mj
Format: Journal Article
Language:English
Published: England 01-05-2021
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
Cohort Studies
Diagnosis, Differential
Female
Humans
Middle Aged
Ovarian Neoplasms - diagnostic imaging
Ovarian Neoplasms - pathology
Predictive Value of Tests
Risk Assessment - methods
Ultrasonography
Young Adult
pathology
ovaries
neoplasms
Ultrasound
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Summary: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. 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. 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. 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. 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.
ISSN:1600-0455
DOI:10.1177/0284185120933990