John C Waterton | PubMed Health

PubMed2003

Dynamic contrast-enhanced MRI of vascular changes induced by the VEGF-signalling inhibitor ZD4190 in human tumour xenografts.

Dynamic contrast-enhanced magnetic resonance imaging (DCEMRI) was used to examine the acute effects of treatment with an inhibitor of vascular endothelial growth factor (VEGF) signaling. ZD4190 is an orally bioavailable inhibitor of VEGF receptor-2 (KDR) tyrosine kinase activity, which elicits broad-spectrum antitumour activity in preclinical models following chronic once-daily dosing. Nude mice, bearing established (0.5-1.0 mL volume) human prostate (PC-3), lung (Calu-6) and breast (MDA-MB-231) tumor xenografts, were dosed with ZD4190 (p.o.) using a 1 day (0 and 22 h) or 7 day (0, 24, 48, 72, 96,120,144, and 166 h) treatment regimen. DCEMRI was employed 2 h after the last dose of ZD4190, using the contrast agent gadopentetate dimeglumine. Dynamic data were fit to a compartmental model to obtain voxelwise K(trans), the transfer constant for gadopentetate into the tumor. K(trans) was averaged over the entire tumor, and a multi-threshold histogram analysis was also employed to account for tumor heterogeneity. Reductions in K(trans) reflect reductions in flow, in endothelial surface area, and/or in vascular permeability. A vascular input function was obtained for each mouse simultaneously with the tumor DCEMRI data. ZD4190 treatment produced a dose-dependent (12.5-100 mg x kg(-1) per dose) reduction in K(trans) in PC-3 prostate tumors. At 100 mg x kg(-1), the largest concentration examined, ZD4190 reduced K(trans) in PC-3 tumors by 31% following 2 doses (1 day treatment regimen; p < 0.001) and by 53% following 8 doses (7 day regimen; p < 0.001). Comparative studies in the three models using a showed similar reductions in K(trans) for the lung and breast tumors using the histogram analysis, although the statistical significance was lost when K(trans) was averaged over the entire tumor. Collectively these studies suggest that DCEMRI using gadopentetate may have potential clinically, for monitoring inhibition of VEGF signaling in solid tumors.

Animals↗

PubMed2002

A minimum description length approach to statistical shape modeling.

We describe a method for automatically building statistical shape models from a training set of example boundaries/surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of "landmarks" manually on each training image, which is time consuming and subjective in two dimensions and almost impossible in three dimensions. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the "best" model. We define "best" as that which minimizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of two-dimensional boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking-the current gold standard. We also show that the method can be extended straightforwardly to three dimensions.

Algorithms↗