TY - JOUR
T1 - Modeling of nanotherapeutics delivery based on tumor perfusion
AU - Van De Ven, Anne L.
AU - Abdollahi, Behnaz
AU - Martinez, Carlos J.
AU - Burey, Lacey A.
AU - Landis, Melissa D.
AU - Chang, Jenny C.
AU - Ferrari, Mauro
AU - Frieboes, Hermann B.
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013/5
Y1 - 2013/5
N2 - Heterogeneities in the perfusion of solid tumors prevent optimal delivery of nanotherapeutics. Clinical imaging protocols for obtaining patient-specific data have proven difficult to implement. It is challenging to determine which perfusion features hold greater prognostic value and to relate measurements to vessel structure and function. With the advent of systemically administered nanotherapeutics whose delivery is dependent on overcoming diffusive and convective barriers to transport, such knowledge is increasingly important. We describe a framework for the automated evaluation of vascular perfusion curves measured at the single vessel level. Primary tumor fragments, collected from triple-negative breast cancer patients and grown as xenografts in mice, were injected with fluorescence contrast and monitored using intravital microscopy. The time to arterial peak and venous delay, two features whose probability distributions were measured directly from time-series curves, were analyzed using a fuzzy c-mean supervised classifier in order to rank individual tumors according to their perfusion characteristics. The resulting rankings correlated inversely with experimental nanoparticle accumulation measurements, enabling the modeling of nanotherapeutics delivery without requiring any underlying assumptions about tissue structure or function, or heterogeneities contained therein. With additional calibration, these methodologies may enable the investigation of nanotherapeutics delivery strategies in a variety of tumor models.
AB - Heterogeneities in the perfusion of solid tumors prevent optimal delivery of nanotherapeutics. Clinical imaging protocols for obtaining patient-specific data have proven difficult to implement. It is challenging to determine which perfusion features hold greater prognostic value and to relate measurements to vessel structure and function. With the advent of systemically administered nanotherapeutics whose delivery is dependent on overcoming diffusive and convective barriers to transport, such knowledge is increasingly important. We describe a framework for the automated evaluation of vascular perfusion curves measured at the single vessel level. Primary tumor fragments, collected from triple-negative breast cancer patients and grown as xenografts in mice, were injected with fluorescence contrast and monitored using intravital microscopy. The time to arterial peak and venous delay, two features whose probability distributions were measured directly from time-series curves, were analyzed using a fuzzy c-mean supervised classifier in order to rank individual tumors according to their perfusion characteristics. The resulting rankings correlated inversely with experimental nanoparticle accumulation measurements, enabling the modeling of nanotherapeutics delivery without requiring any underlying assumptions about tissue structure or function, or heterogeneities contained therein. With additional calibration, these methodologies may enable the investigation of nanotherapeutics delivery strategies in a variety of tumor models.
UR - http://www.scopus.com/inward/record.url?scp=84878251191&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878251191&partnerID=8YFLogxK
U2 - 10.1088/1367-2630/15/5/055004
DO - 10.1088/1367-2630/15/5/055004
M3 - Article
AN - SCOPUS:84878251191
SN - 1367-2630
VL - 15
JO - New Journal of Physics
JF - New Journal of Physics
M1 - 055004
ER -