Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm

Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm

Abstract 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 metastat...

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Bibliographic Details
Main Authors: Ashleigh Ledger, Jolien Ceusters, Lil Valentin, Antonia Testa, Caroline Van Holsbeke, Dorella Franchi, Tom Bourne, Wouter Froyman, Dirk Timmerman, Ben Van Calster
Format: Article
Language:English
Published: BMC 2023-11-01
Series:BMC Medical Research Methodology
Subjects:
Ovarian Neoplasms
Prediction models
Machine learning
Calibration
Multiclass models
Online Access:https://doi.org/10.1186/s12874-023-02103-3

Internet

https://doi.org/10.1186/s12874-023-02103-3

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