Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

TL;DR

This paper discusses how quantitative in vivo imaging can be used to calibrate mathematical models for personalized tumor forecasting and treatment optimization, addressing the challenge of cancer heterogeneity.

Contribution

It summarizes imaging data types, computational methods, and barriers for implementing personalized tumor prediction models in clinical oncology.

Findings

Imaging data can be used to calibrate patient-specific cancer models

Computational methods enable in silico tumor forecasting

Personalized models can potentially improve treatment outcomes

Abstract

Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor 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 tumor 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 tumor 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.