In fact, there are exciting initial studies available for using retrospectively registered PET–MRI data to diagnose breast lesions [81]. (Note: here we use “retrospective”
in the sense of using separate PET and MRI scanners and performing the registration off-line.) Moy et al. found that when the (clinical) DCE-MRI and (prone) FDG-PET data were combined, there were marked improvements in several of the standard diagnostic statistics. For example, the sensitivity was 83% (up from 57% for PET alone), the specificity was 97% (up from 53% for MRI alone), the positive predictive value was 98% (up from 77% for MRI alone), and the negative predictive value was 80% (up from Selleckchem PLX4032 59% for PET alone). Furthermore, the false-negative rate was reduced to 9% (down from 27% for PET alone). In light of these results, it is not an unreasonable hypothesis that combined PET–MRI will facilitate more accurate and precise monitoring and prediction of response in the therapeutic setting. Collecting quantitative, multimodal, multiparametric data also presents the opportunity to perform basic cancer biology studies. For example, studying how the individual parameters change spatially and temporally could enable the formation of hypotheses related to how individual pharmaceuticals
work in vivo. CDK inhibitor The different measurements report on different aspects of the same treatment, so it may be possible to visualize (noninvasively) the various downstream effects (i.e., drug activity) of a given therapeutic regimen. Furthermore, it may be possible to form hypotheses on an individual
basis, thereby contributing to personalized medicine in a very practical manner. There is also the ability to develop fundamental imaging science. By studying how the quantitative parameters change spatially and temporally, it may be possible to learn more about the appropriate interpretation of the parameters themselves by cross-validation and visualization. For example, simple correlation analysis of various parameters Resveratrol may provide insights into their relationship which can subsequently be used to more comprehensively characterize the tissue giving rise to those measures. For example, by combining measurements of DW-MRI and 18F-fluodeoxythymidine PET, it may be able possible to determine the overall proliferative capacity for a given section of tissue. By synthesizing data from DCE-MRI and 18F-fluoromisonidazole PET, we may be able to elucidate the temporal and spatial relationship between angiogenesis and hypoxia in vivo. While there are some initial studies that have been contributed in the literature [82], [83] and [84], this is currently an underexplored area of research. Finally, spatially and temporally integrated PET–MRI data present the opportunity to perform practical — clinically relevant — imaging-guided mathematical modeling of tumor growth [85].