cancer imaging
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Cancer Imaging
Topics in Bioengineering
The present and future role of cancer imaging
Fass L. (2008) Mol Oncol. Figures 1 & 2
Shrinidhi
Urano et al. (2011) Science Transl Med.
Figure 1
Michael
Urano et al. (2011) Science Transl Med.
Figure 1
Michael
Urano et al. (2011) Science Transl Med.
Figure 2
Paige
Anna
Urano et al. (2011) Science Transl Med.
Figure 3
Urano et al. (2011) Science Transl Med.
Figure 4
Felix
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
Urano et al. (2011) Science Transl Med.
Supplementary Data
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.
Michael
http://www.ucl.ac.uk/surgicalscience/departments_research/gsrg/nmlc/newsarchive http://www.docstoc.com/docs/84445901/Elastic-Scattering-Spectroscopy-_-Light-Scattering-Spectroscopy-
Elastic Scattering SpectroscopyNon-dysplastic intestinal metaplasia
High grade dysplasia
Zhu et al. (2009) J Biomed Opt. Figure 1
Paige
Zhu et al. (2009) J Biomed Opt. Figure 2
Shrinidhi
Zhu et al. (2009) J Biomed Opt. Figure 3
Anna
http://en.wikipedia.org/wiki/Principal_component_analysis
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
Zhu et al. (2009) J Biomed Opt. Figure 4
Felix
Zhu et al. (2009) J Biomed Opt. Figure 5
Kevin
Zhu et al. (2009) J Biomed Opt. Table 1
Zhu et al. (2009) J Biomed Opt. Figure 6
Maura