TY - JOUR
T1 - A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions
AU - Stamatelos, Spyros K.
AU - Kim, Eugene
AU - Pathak, Arvind P.
AU - Popel, Aleksander S.
N1 - Funding Information:
Research supported by NIH grant R01 CA138264 and Susan G. Komen for the Cure Grant KG090640 . The authors would like to thank Dr. Corban Rivera for his help with Module 1 of the bioimage informatics algorithm and the useful discussions about traversal algorithms.
PY - 2014/1
Y1 - 2014/1
N2 - Induction of tumor angiogenesis is among the hallmarks of cancer and a driver of metastatic cascade initiation. Recent advances in high-resolution imaging enable highly detailed three-dimensional geometrical representation of the whole-tumor microvascular architecture. This enormous increase in complexity of image-based data necessitates the application of informatics methods for the analysis, mining and reconstruction of these spatial graph data structures. We present a novel methodology that combines ex-vivo high-resolution micro-computed tomography imaging data with a bioimage informatics algorithm to track and reconstruct the whole-tumor vasculature of a human breast cancer model. The reconstructed tumor vascular network is used as an input of a computational model that estimates blood flow in each segment of the tumor microvascular network. This formulation involves a well-established biophysical model and an optimization algorithm that ensures mass balance and detailed monitoring of all the vessels that feed and drain blood from the tumor microvascular network. Perfusion maps for the whole-tumor microvascular network are computed. Morphological and hemodynamic indices from different regions are compared to infer their role in overall tumor perfusion.
AB - Induction of tumor angiogenesis is among the hallmarks of cancer and a driver of metastatic cascade initiation. Recent advances in high-resolution imaging enable highly detailed three-dimensional geometrical representation of the whole-tumor microvascular architecture. This enormous increase in complexity of image-based data necessitates the application of informatics methods for the analysis, mining and reconstruction of these spatial graph data structures. We present a novel methodology that combines ex-vivo high-resolution micro-computed tomography imaging data with a bioimage informatics algorithm to track and reconstruct the whole-tumor vasculature of a human breast cancer model. The reconstructed tumor vascular network is used as an input of a computational model that estimates blood flow in each segment of the tumor microvascular network. This formulation involves a well-established biophysical model and an optimization algorithm that ensures mass balance and detailed monitoring of all the vessels that feed and drain blood from the tumor microvascular network. Perfusion maps for the whole-tumor microvascular network are computed. Morphological and hemodynamic indices from different regions are compared to infer their role in overall tumor perfusion.
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U2 - 10.1016/j.mvr.2013.12.003
DO - 10.1016/j.mvr.2013.12.003
M3 - Article
C2 - 24342178
AN - SCOPUS:84892369444
SN - 0026-2862
VL - 91
SP - 8
EP - 21
JO - Microvascular Research
JF - Microvascular Research
ER -