FEASIBILITY OF USING RAMAN-BASED TECHNIQUES FOR BREAST CANCER DETECTION

Authors

Sara Mollamohammada, University of Nebraska-LincolnFollow

Date of this Version

12-2015

Document Type

Article

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Civil Engineering, Under the Supervision of Professor Tian C. Zhang. Lincoln, Nebraska: December 2015

Copyright (c) 2015 Sara Mollamohammada

Abstract

Breast cancer is one of the common types of cancer among women all over the world. Early diagnosis is an effective way that improve the treatment process and gives the patients a better chance of survival. Many of the patients infected by breast cancer choose breast conservation surgery (BCS). However, some of those will be subjected to mastectomy, and many will have tumor recurrence as there is no precise technique to show the tumor margins. Raman-based methods are powerful techniques with potential to rapidly differentiate normal from tumor tissues and provides a solution to detect tumor margin. This is because the Raman signals provide unique information about a sample and has a molecular fingerprint effect.

This study evaluated Raman Spectroscopy (RS), Surface Enhanced Raman Spectroscopy (SERS), and Coherent Anti Stokes Raman Spectroscopy (CARS) as practical techniques to differentiate normal and cancerous cells and detect breast tumor margin, by using mouse and human tissues as models. Thin excised normal and cancerous tissues from mature mouse and human were sliced and fixed on glass and gold slides, and coated with silver and gold nanoparticles. SERS and CARS spectra, and CARS imaging were detected and analyzed with a Raman and CARS Spectrometer. After that, the results were analyzed using the two-band ratio model, principal component analysis (PCA), and principal component detruded fluctuation analysis (PC-DFA) to find the difference in Raman and CARS signals of the two groups of tissues. Results from the spectrum/imaging of each type of tissues and comparison of different tissues indicate that the RS, SERS and CARS are viable techniques to differentiate between normal and cancer tissues. Also, the tissues with small thickness (around 5 micron) and smooth surface are more appropriate for all Raman-based techniques. In addition, CARS shows a strong capability to image the tissues and to demonstrate the concentration distribution of lipids, and hence, can be used to distinguish normal and cancer cells. Furthermore, CARS spectroscopy is capable of providing information about the tissues in specific wavelength ranges (e.g. lipids), which makes it an appropriate technique for clinical use to collect information about different stages of cancer from human tissue models within a short time.

Advisor: Tian C. Zhang