Dünger Lukas, Mäusel Emily, Zwanenburg Alex, Löck Steffen
OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
National Center for Tumor Diseases (NCT), NCT/UCC Dresden, a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany.
Phys Imaging Radiat Oncol. 2025 May 7;34:100774. doi: 10.1016/j.phro.2025.100774. eCollection 2025 Apr.
Outcome models predicting e.g. survival, tumour control or radiation-induced toxicities play an important role in the field of radiation oncology. These models aim to support the clinical decision making and pave the way towards personalised treatment. Both validity and reliability of their output are required to facilitate clinical integration. However, models are influenced by uncertainties, arising from data used for model development and model parameters, among others. Therefore, quantifying model uncertainties and addressing their causes promotes the creation of models that are sufficiently reliable for clinical use. This topical review aims to summarise different types and possible sources of uncertainties, presents uncertainty quantification methods applicable to various modelling approaches, and highlights central challenges that need to be addressed in the future.
预测例如生存率、肿瘤控制或辐射诱导毒性等的结果模型在放射肿瘤学领域发挥着重要作用。这些模型旨在支持临床决策，并为个性化治疗铺平道路。为便于临床整合，需要其输出结果的有效性和可靠性。然而，模型受到多种不确定性的影响，这些不确定性源于用于模型开发的数据和模型参数等。因此，量化模型不确定性并解决其成因有助于创建足够可靠以供临床使用的模型。本专题综述旨在总结不同类型和可能的不确定性来源，介绍适用于各种建模方法的不确定性量化方法，并突出未来需要解决的核心挑战。
