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Cancer Imaging. Topics in Bioengineering. The present and future role of cancer imaging. Fass L. (2008) Mol Oncol . Figures 1 & 2. Shrinidhi. Michael. Urano et al. (2011) Science Transl Med. Figure 1. Michael. Urano et al. (2011) Science Transl Med. Figure 1. Paige.
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Cancer Imaging Topics in Bioengineering
The present and future role of cancer imaging Fass L. (2008) Mol Oncol. Figures 1 & 2
Michael Urano et al. (2011) Science Transl Med. Figure 1
Michael Urano et al. (2011) Science Transl Med. Figure 1
Paige Urano et al. (2011) Science Transl Med. Figure 2
Anna Urano et al. (2011) Science Transl Med. Figure 3
Felix Urano et al. (2011) Science Transl Med. Figure 4
Kevin Urano et al. (2011) Science Transl Med. Figure 5 Urano et al. (2011) Science Transl Med. Figure 5
Inseong Urano et al. (2011) Science Transl Med. Figure 6
Movies!!! • http://stm.sciencemag.org/content/3/110/110ra119/suppl/DC1 • Video S1 (.mov format). Dynamic fluorescence endoscopy of SHIN3 metastases. • Video S2 (.mov format). Dynamic fluorescence endoscopy of SKOV3 metastases. • Video S3 (.mov format). Dynamic fluorescence endoscopy of OVCAR3 metastases. • Video S4 (.mov format). Dynamic fluorescence endoscopy of OVCAR4 metastases. • Video S5 (.mov format). Dynamic fluorescence endoscopy of OVCAR5 metastases. • Video S6 (.mov format). Dynamic fluorescence endoscopy of OVCAR8 metastases. • Video S7 (.mov format). Fluorescence endoscopy of six ovarian cancer metastases 60 min after spraying the gGlu-HMRG probe. • Video S8 (.mov format). Dynamic fluorescence endoscopy–guided biopsy of tiny peritoneal SHIN3 ovarian metastases. Urano et al. (2011) Science Transl Med. Supplementary Data
Elastic Scattering Spectroscopy Non-dysplastic intestinal metaplasia High grade dysplasia http://www.ucl.ac.uk/surgicalscience/departments_research/gsrg/nmlc/newsarchive http://www.docstoc.com/docs/84445901/Elastic-Scattering-Spectroscopy-_-Light-Scattering-Spectroscopy-
Paige Zhu et al. (2009) J Biomed Opt. Figure 1
Shrinidhi Zhu et al. (2009) J Biomed Opt. Figure 2
Anna Zhu et al. (2009) J Biomed Opt. Figure 3
Principal Component Analysis • Used for predictive models • Converts set of observations (data) of possibly correlated variables into values of linearly uncorrelated (ie, orthogonal) variables (“principal components”) • First PC accounts for as much of variability in data as possible, each subsequent PC is less (and still orthogonal) • Reveals internal structure of data in a way that best describes its variance http://en.wikipedia.org/wiki/Principal_component_analysis
Felix Zhu et al. (2009) J Biomed Opt. Figure 4
Kevin Zhu et al. (2009) J Biomed Opt. Figure 5
Maura Zhu et al. (2009) J Biomed Opt. Figure 6