Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia 20057, USA.
Am J Pathol. 2013 Feb;182(2):312-8. doi: 10.1016/j.ajpath.2012.09.024. Epub 2012 Dec 4.
Biologically accurate mouse models of human cancer have become important tools for the study of human disease. The anatomical location of various target organs, such as brain, pancreas, and prostate, makes determination of disease status difficult. Imaging modalities, such as magnetic resonance imaging, can greatly enhance diagnosis, and longitudinal imaging of tumor progression is an important source of experimental data. Even in models where the tumors arise in areas that permit visual determination of tumorigenesis, longitudinal anatomical and functional imaging can enhance the scope of studies by facilitating the assessment of biological alterations, (such as changes in angiogenesis, metabolism, cellular invasion) as well as tissue perfusion and diffusion. One of the challenges in preclinical imaging is the development of infrastructural platforms required for integrating in vivo imaging and therapeutic response data with ex vivo pathological and molecular data using a more systems-based multiscale modeling approach. Further challenges exist in integrating these data for computational modeling to better understand the pathobiology of cancer and to better affect its cure. We review the current applications of preclinical imaging and discuss the implications of applying functional imaging to visualize cancer progression and treatment. Finally, we provide new data from an ongoing preclinical drug study demonstrating how multiscale modeling can lead to a more comprehensive understanding of cancer biology and therapy.
生物医学上精确的人类癌症小鼠模型已成为人类疾病研究的重要工具。各种目标器官的解剖位置，如脑、胰腺和前列腺，使得疾病状态的确定变得困难。成像方式，如磁共振成像，可以极大地增强诊断，并且肿瘤进展的纵向成像也是实验数据的重要来源。即使在肿瘤发生在允许肉眼确定肿瘤形成的区域的模型中，纵向解剖学和功能成像也可以通过促进对生物变化（如血管生成、代谢、细胞浸润的变化）以及组织灌注和扩散的评估，来增强研究的范围。临床前成像的挑战之一是开发基础设施平台，该平台需要使用更基于系统的多尺度建模方法将体内成像和治疗反应数据与体外病理和分子数据集成。进一步的挑战在于整合这些数据进行计算建模，以更好地理解癌症的病理生物学，并更好地影响其治疗效果。我们回顾了临床前成像的当前应用，并讨论了应用功能成像来可视化癌症进展和治疗的意义。最后，我们提供了正在进行的临床前药物研究的新数据，展示了多尺度建模如何能够更全面地理解癌症生物学和治疗。
