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Cancer, Complexity, ComputationQuantitative In Vivo Imaging to Enable Tumour Forecasting and Treatment Optimization

Cancer, Complexity, Computation: Quantitative In Vivo Imaging to Enable Tumour Forecasting and... [Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumour status and treatment outcomes. However, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate the use of quantitative in vivo imaging data to calibrate mathematical models for the personalized forecasting of tumour development. In this chapter, we summarize the main data types available from both common and emerging in vivo medical imaging technologies, and how these data can be used to obtain patient-specific parameters for common mathematical models of cancer. We then outline computational methods designed to solve these models, thereby enabling their use for producing personalized tumour forecasts in silico, which, ultimately, can be used to not only predict response, but also optimize treatment. Finally, we discuss the main barriers to making the above paradigm a clinical reality.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Cancer, Complexity, ComputationQuantitative In Vivo Imaging to Enable Tumour Forecasting and Treatment Optimization

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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-04378-9
Pages
55 –97
DOI
10.1007/978-3-031-04379-6_3
Publisher site
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Abstract

[Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumour status and treatment outcomes. However, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate the use of quantitative in vivo imaging data to calibrate mathematical models for the personalized forecasting of tumour development. In this chapter, we summarize the main data types available from both common and emerging in vivo medical imaging technologies, and how these data can be used to obtain patient-specific parameters for common mathematical models of cancer. We then outline computational methods designed to solve these models, thereby enabling their use for producing personalized tumour forecasts in silico, which, ultimately, can be used to not only predict response, but also optimize treatment. Finally, we discuss the main barriers to making the above paradigm a clinical reality.]

Published: Aug 12, 2022

Keywords: Cancer; Computational oncology; Magnetic resonance imaging; Finite element analysis; Isogeometric analysis; Finite differences; Model selection; Sensitivity analysis; Inverse problems; Patient-specific models; Optimal control theory

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