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.

中文翻译：

---

定量体内成像可实现肿瘤预测和治疗优化

当前肿瘤学的临床决策依靠大量患者的平均值来评估肿瘤状态和治疗结果。但是，癌症表现出内在的进化异质性，需要基于对癌症生长和治疗反应的严格而精确的预测的个体方法。为此，我们提倡使用体内定量成像数据来校准数学模型，以进行肿瘤发展的个性化预测。在本章中，我们总结了可从常见和新兴的体内医学成像技术中获得的主要数据类型，以及如何将这些数据用于获得癌症的常见数学模型的患者特定参数。然后，我们概述了旨在解决这些模型的计算方法，从而使它们可用于通过计算机生成个性化的肿瘤预测，最终可以不仅用于预测反应，而且可以优化治疗方法。最后，我们讨论了使上述范例成为现实的主要障碍。