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arXiv 2102.12602q-bio.TOcs.CEq-bio.QM
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定量体内成像可实现肿瘤预测和治疗优化

Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

Guillermo Lorenzo, David A. Hormuth, Angela M. Jarrett, Ernesto A. B. F. Lima, Shashank Subramanian, George Biros, J. Tinsley Oden, Thomas J. R. Hughes, Thomas E. Yankeelov

摘要:

目前肿瘤学的临床决策依赖于大量患者群体的平均值来评估肿瘤状态和治疗结果。然而,癌症表现出固有的不断发展的异质性,需要基于对癌症生长和治疗反应的严格和精确预测的个体方法。为此,我们提倡使用定量的体内成像数据来校准数学模型,用于肿瘤发展的个性化预测。在本章中,我们总结了常见和新兴的体内医学成像技术的主要数据类型,以及如何使用这些数据来获得常见癌症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,从而使其能够用于在计算机上生成个性化的肿瘤预测,最终不仅可用于预测反应,还可用于优化治疗。最后,我们讨论了使上述范式成为临床现实的主要障碍。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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