Automated raman cytology for the classification of triple negative breast cancer cell lines

Kevin O'Dwyer; Katarina Domijan; Adam Dignam; Zhengyuan Tang; Marion Butler; Bryan M. Hennelly

doi:10.1117/12.2601241

9 October 2021 Automated raman cytology for the classification of triple negative breast cancer cell lines

Kevin O'Dwyer, Katarina Domijan, Adam Dignam, Zhengyuan Tang, Marion Butler, Bryan M. Hennelly

Proceedings Volume 11900, Optics in Health Care and Biomedical Optics XI; 1190023 (2021) https://doi.org/10.1117/12.2601241
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Conference Poster

Abstract

Raman micro-spectroscopy is an optical technique that can provide information on the biochemical composition of biological cells. Raman cytology, whereby Raman spectra are recorded from the nucleus of human epithelial cells is an active area of research. This typically involves the application of multivariate statistical algorithms to classify cell type, or disease group, based on the Raman spectrum. Although this approach has been shown to improve the diagnostic sensitivity of clinical cervical, bladder, and oral cytology for the identification of cancer cells, there has been no clinical adoption to date. The main reasons for this are the slow recording time and lack of reproducibility. In this paper, we review a recently proposed automated Raman cytology system based on image processing that can record several thousands of cell nuclei/day. The automation process is implemented using an open-source microscopy control system called Micro-Manager, which can readily be adapted by those with existing Raman microscopes and is designed to target the unstained nucleus, identified by imaging a plane below the sample, which is the primary target for Raman cytology based cancer diagnostics. In this paper we investigate the application of automated Raman cytology for the classification of two sub-types of Triple Negative Breast Cancer Cell Lines prepared using the ThinPrep protocol and we discuss how this approach could potentially improve the diagnostic sensitivity of Fine Needle Aspiration Cytology for Breast Cancer Diagnosis.

Citation Download Citation

Kevin O'Dwyer, Katarina Domijan, Adam Dignam, Zhengyuan Tang, Marion Butler, and Bryan M. Hennelly "Automated raman cytology for the classification of triple negative breast cancer cell lines", Proc. SPIE 11900, Optics in Health Care and Biomedical Optics XI, 1190023 (9 October 2021); https://doi.org/10.1117/12.2601241

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