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Optical Detection of Epithelial Cancer

Optical Detection of Epithelial Cancer. Identification of optical diagnostic markers of early epithelial cancer. Names: Jesse Eaton Il Won Chang Lab Mentor: Antonio Varone Faculty Lab: Prof. Irene Georgakoidi. Objective. D evelop a non-invasive way to detect pre-cancerous cells.

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Optical Detection of Epithelial Cancer

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  1. Optical Detection of Epithelial Cancer Identification of optical diagnostic markers of early epithelial cancer. Names: Jesse Eaton Il Won Chang Lab Mentor: Antonio Varone Faculty Lab: Prof. Irene Georgakoidi

  2. Objective • Develop a non-invasive way to detect pre-cancerous cells. Hypothesis • Image processing techniques can detect differences between normal and HPV infected cells by analyzing pictures of NADH and FAD+ concentration at the cellular level.

  3. Clinical Need • Cervical Cancer is relatively easy to remove once identified • Current methods for detecting cancer are often invasive and not conclusive. • Over 90% of all cervical cancer is estimated to be caused by the human papillomavirus (HPV) www.swedish.org

  4. General Outline • Human Foreskin Keratinocytes • Infect some with the human papillomavirus • Culture cells until there are enough • Add Ca2+ conditional medium to induce differentiation • Image cells during different days

  5. HPV Infected Cells • HPV stitches the genes E6 and E7 into a cervical cancer cell • E6 and E7 code for proteins that deactivate p53 and pRb respectively • Proteins p53 and pRb are “tumor suppressor protein” that regulate the cell cycle

  6. Imaging Cells ? Day 5 Day 3 Day 1 www.wikipedia.org

  7. Image Processing Original cell image Autocorrelation (2D) Power Spectral Density (PSD) Autocorrelation (1D Cut)

  8. Results

  9. Sources of Error • Most cells died before day 5 • Incubator was shut off for multiple days (accidently) • Images were not perfect: It was our first time Googlechromesupportnow.com

  10. Conclusion • Normal HFK cells showed a decrease in their ß value while HPV infected cells had a slight increase in ß value. • This difference could confirmed by analyzing cells for more days.

  11. Future Work • Culture cells in three dimensions to represent real epithelial tissue • MATLAB code that processes a stack of 2D images (3D image processing)

  12. Questions?

  13. Reference: Graph of autocorrelation function on slide 5 was taken from a paper By Douglas MacDonald, Martin Hunter, Kyle Quinn, and Irene Georgakoudi. This paper was entitled “Fractal dimension in irregular regions of biomedical images” < en.wikipedia.org/wiki/Cervical_cancer > KyleQuin‘s Matlab code: KyleAnnulus.m Paper on fractals: < www.jfgouyet.fr/fractal/fractauk/chapter2.pdf > < http://en.wikipedia.org/wiki/Epidermis_(skin) > < http://www.swedish.org/Services/Robotic-Surgery-Program/Conditions-We-Treat/Cervical-Cancer#axzz2EbcLdOVg > < Googlechromesupportnow.com >

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