DEVELOPMENT AND ASSESSMENT OF TOOLS FOR QUANTITATIVE DYNAMIC CONTRAST ENHANCED MRI OF BONE METASTASES FROM BREAST AND PROSTATE CANCER

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The microenvironment of solid human tumors is characterized by
heterogeneity in oxygenation and by proliferation of a network of blood
vessels that provides oxygen and nutrients and removes waste products.

The capability of a tumor to metastasize is linked to the well
known hyper permeability of tumor vessels; in fact angiogenic primary
tumors possess a large number of micro-vessels through which the
metastasizing cells are shed into the blood stream. Pioneering studies
performed by Folkman in 1971 proposed an insightful anticancer therapy
by starvation of blood supply, Folkman’s intuition that tumor growth
and metastasis strictly depend on angiogenesis led to the idea that
blocking tumor nourishment could be one of the ways to avoid its
spread. Many imaging strategies have been used to determine
angiogenesis in vivo, among them Dynamic Contrast Enhanced MRI
(DCE-MRI) which provides a powerful tool for the rapid evaluation of
the acute pharmacodynamic effect of the most recent agents in clinical
trials, most notably in the

case of mechanisms that affect tumor perfusion. One of the
attractives of dynamic post contrast imaging is the insight it offers
into the distribution kinetics of contrast agent in the tissue. These
quantities are generally derived from simple models of the tissue as a
compartmentalized system (usually a plasma-interstitial
two-compartments model is used), using a kinetic analysis originally
developed for use with nuclear medicine tracers. In this study a three
parameters model has been used in order to describe the transport of
tracer in the tissues. The first one takes into account the plasma
volume in the voxel or in the region of interest being examined, the
second is related to the amount of tracer that enters the EES and the
last one determines the washout rate from EES back into the blood
plasma. An artery input function (AIF), namely the concentration of
contrast in the plasma, is provided as well. In order to obtain these
parameters it is necessary to perform the fit of the chosen model on
the concentration-curves vs time. It is possible to correlate the
contrast agent concentration with the difference of relaxation rate
(the inverse of longitudinal relaxation time); this way the
concentration-curves can be achieved by means of T1-weighted images. In
this work, an acquisition protocol has been optimized in order to
provide the images from which extracting the data related to the tumor
perfusion. Post processing tools, which carry out the fitting, the
smoothing, and, the registration, have been developed and tested, as
well. These tools can provide the oncologysts and radiologists a way to
perform reproducible and quantitative estimation of the tumor perfusion
in anti-angiogenic therapy.

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