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