Prospective evaluation of IOTA logistic regression models LR1 and LR2 in comparison with subjective pattern recognition for diagnosis of ovarian cancer in an outpatient setting.

The IOTA models maintained their high sensitivity when used in an outpatient setting. Specificity was relatively low, which indicates that a significant proportion of the women would have been offered unnecessary surgery for suspected ovarian cancer. These findings show that the IOTA models could be used as a first-stage test to diagnose ovarian cancer in an outpatient setting, but a different second-stage test is required to minimize the number of false-positive findings. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.