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.

翻译：目前肿瘤学临床决策依靠大量病人的平均数来评估肿瘤状况和治疗结果。然而,癌症呈现出一种内在的不断变化的异质性,需要基于对癌症增长和治疗反应的严格和精确预测的个别方法。为此目的,我们主张使用活体成像定量数据来校准肿瘤发展个人化预测的数学模型。在本章中,我们总结了从常见和新出现的体外医学成像技术中可获得的主要数据类型,以及如何利用这些数据为常见癌症数学模型获取针对病人的参数。我们然后概述了旨在解决这些模型的计算方法,从而使这些模型能够用于在硅进行个性化肿瘤预测,最终不仅用于预测反应,而且用于优化治疗。最后,我们讨论了使上述模型成为临床现实的主要障碍。