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...

Deskribapen osoa

Xehetasun bibliografikoak
Egile Nagusiak: Ashleigh Ledger, Jolien Ceusters, Lil Valentin, Antonia Testa, Caroline Van Holsbeke, Dorella Franchi, Tom Bourne, Wouter Froyman, Dirk Timmerman, Ben Van Calster
Formatua: Artikulua
Hizkuntza:English
Argitaratua: BMC 2023-11-01
Saila:BMC Medical Research Methodology
Gaiak:
Ovarian Neoplasms
Prediction models
Machine learning
Calibration
Multiclass models
Sarrera elektronikoa:https://doi.org/10.1186/s12874-023-02103-3

Internet

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

Antzeko izenburuak