Multiclass risk models for ovarian malignancy : an illustration of prediction uncertainty due to the choice of algorithm
Background: Assessing malignancy risk is important to choose appropriate management of ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian tumor is benign, borderline malignant, stage I primary invasive, stage II-IV primary invasive, or secondary metastatic. Methods: This retrospective cohort study used 5909 patients recruited from 1999 to 2012 for model develo
