An evaluation of uncertainty quantification methods and measures for deep learning outcome prediction models in head and neck cancer radiotherapy

Background and Purpose: Deep learning (DL) outcome prediction models show promise in radiotherapy but face limited clinical adoption due to concerns about prediction reliability. Although uncertainty quantification (UQ) can provide confidence estimates alongside predictions, there is currently littl...

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Bibliografische gegevens

Hoofdauteurs:	Daniel C. MacRae, Luuk van der Hoek, Joëlle E. van Aalst, Suzanne P.M de Vette, Robert van der Wal, Hendrike Neh, Baoqiang Ma, Nanna M. Sijtsema, Matias A. Valdenegro-Toro, Peter M.A. van Ooijen, Lisanne V. van Dijk
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Elsevier 2026-05-01
Reeks:	Physics and Imaging in Radiation Oncology
Onderwerpen:	Outcome prediction Deep learning Uncertainty quantification NTCP TCP
Online toegang:	http://www.sciencedirect.com/science/article/pii/S2405631626000783
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