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Uncertainties in outcome modelling in radiation oncology
Lukas Dünger1, Emily Mäusel1, Alex Zwanenburg1,2
1OncoRay - 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.
Outcome models in radiation oncology are crucial for personalized treatment but face uncertainties. This review details uncertainty types, quantification methods, and future challenges for reliable clinical integration of these predictive models.
Area of Science:
- Radiation oncology
- Medical physics
- Biostatistics
Background:
- Outcome models are vital for predicting patient results like survival and toxicity in radiation oncology.
- These models support clinical decision-making and personalized medicine.
- Model reliability is essential for clinical integration, but uncertainties pose a challenge.
Purpose of the Study:
- To review the types and sources of uncertainties in outcome models.
- To present methods for quantifying these uncertainties.
- To highlight future challenges for reliable model development and clinical use.
Main Methods:
- Literature review of uncertainty quantification in predictive modeling.
- Categorization of uncertainty types and sources.
- Discussion of applicable methods for various modeling approaches.
Main Results:
- Identified various sources of uncertainty, including data and model parameters.
- Summarized diverse uncertainty quantification techniques.
- Highlighted key challenges in addressing model uncertainties.
Conclusions:
- Quantifying and addressing uncertainties is critical for developing reliable outcome models.
- Reliable models are necessary for successful clinical integration in radiation oncology.
- Further research is needed to overcome identified challenges.
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