1. Cancer Imaging Topics in Bioengineering

2. The present and future role of cancer imaging Fass L. (2008) Mol Oncol. Figures 1 & 2

3. Shrinidhi

4. Urano et al. (2011) Science Transl Med. Figure 1 Michael

5. Urano et al. (2011) Science Transl Med. Figure 1 Michael

6. Urano et al. (2011) Science Transl Med. Figure 2 Paige

7. Anna Urano et al. (2011) Science Transl Med. Figure 3

8. Urano et al. (2011) Science Transl Med. Figure 4 Felix

9. Kevin Urano et al. (2011) Science Transl Med. Figure 5

10. Inseong Urano et al. (2011) Science Transl Med. Figure 6

11. 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 S1 Video S2 (.mov format). Dynamic fluorescence endoscopy of SKOV3 metastases. Video S2 Video S3 (.mov format). Dynamic fluorescence endoscopy of OVCAR3 metastases. Video S3 Video S4 (.mov format). Dynamic fluorescence endoscopy of OVCAR4 metastases. Video S4 Video S5 (.mov format). Dynamic fluorescence endoscopy of OVCAR5 metastases. Video S5 Video S6 (.mov format). Dynamic fluorescence endoscopy of OVCAR8 metastases. Video S6 Video S7 (.mov format). Fluorescence endoscopy of six ovarian cancer metastases 60 min after spraying the gGlu-HMRG probe. Video S7 Video S8 (.mov format). Dynamic fluorescence endoscopy–guided biopsy of tiny peritoneal SHIN3 ovarian metastases. Video S8

12. Michael

13. 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 Spectroscopy Non-dysplastic intestinal metaplasia High grade dysplasia

14. Zhu et al. (2009) J Biomed Opt. Figure 1 Paige

15. Zhu et al. (2009) J Biomed Opt. Figure 2 Shrinidhi

16. Zhu et al. (2009) J Biomed Opt. Figure 3 Anna

17. 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

18. Zhu et al. (2009) J Biomed Opt. Figure 4 Felix

19. Zhu et al. (2009) J Biomed Opt. Figure 5 Kevin

20. Zhu et al. (2009) J Biomed Opt. Table 1

21. Zhu et al. (2009) J Biomed Opt. Figure 6 Maura