The Need for Credibility Guidance for Analyses Quantifying Margin and Uncertainty

Schroeder, Benjamin; Hund, Lauren; Kittinger, Robert

doi:10.1007/978-3-030-12075-7_2

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…Once a model is developed and calibrated, its predictive capability needs to be assessed through model validation, whereby a model prediction is compared against new data to evaluate its accuracy [203,251]. To assess the validity of the model, one must select a metric to compute the error between the model prediction and data.…”

Section: Limitations Of Model Selection Validation and Uncertainty Qu...mentioning

confidence: 99%

“…If the error is below the desired tolerance, the model is deemed valid. As discussed in [251], the validation can only provide supporting evidence of the model's predictive capability. It is important to realize that the limited availability of the appropriate data directly influences the ability to quantitatively validate the model under investigation.…”

Section: Limitations Of Model Selection Validation and Uncertainty Qu...mentioning

confidence: 99%

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Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Lorenzo

Hormuth

et al. 2022

Biophysics Reviews

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Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

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Section: Limitations Of Model Selection Validation and Uncertainty Qu...mentioning

confidence: 99%

Section: Limitations Of Model Selection Validation and Uncertainty Qu...mentioning

confidence: 99%

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Lorenzo

Hormuth

et al. 2022

Biophysics Reviews

View full text Add to dashboard Cite

show abstract

The Need for Credibility Guidance for Analyses Quantifying Margin and Uncertainty

Cited by 1 publication

References 21 publications

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

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