TY - JOUR
T1 - Preclinical magnetic resonance imaging and systems biology in cancer research
T2 - Current applications and challenges
AU - Albanese, Chris
AU - Rodriguez, Olga C.
AU - Vanmeter, John
AU - Fricke, Stanley T.
AU - Rood, Brian R.
AU - Lee, Yichien
AU - Wang, Sean S.
AU - Madhavan, Subha
AU - Gusev, Yuriy
AU - Petricoin, Emanuel F.
AU - Wang, Yue
N1 - Funding Information:
This work was supported by NIH grants R01CA129003 (C.A.), P30CA51008 (L.W.), HHSN2612200800001E (R.C., S.M. and Y.W.), the in Silico Center for Research Excellence (ISRCE) (R.C., S.M. and Y.W.), the Advance Brain Cancer Cure Foundation (C.A.) and the College of Science at George Mason University (E.F.P.).
PY - 2013/2
Y1 - 2013/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84872739517&partnerID=8YFLogxK
U2 - 10.1016/j.ajpath.2012.09.024
DO - 10.1016/j.ajpath.2012.09.024
M3 - Short survey
AN - SCOPUS:84872739517
SN - 0002-9440
VL - 182
SP - 312
EP - 318
JO - American Journal of Pathology
JF - American Journal of Pathology
IS - 2
ER -