Analysis of Cancer Margins in Mass Spectrometry Images

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Authors

Katherine Williams

Date

2024-08-28

Type

thesis

Language

eng

Keyword

Mass Spectrometry Imaging , Breast Cancer , Skin Cancer , Biomedical Computing , Tumor Stroma , Machine Learning , Computing

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Abstract

Computational analysis of metabolite activity surrounding a tumor border could enhance intraoperative tumor resection. One method to collect this data is mass spectrometry imaging, an analytical technique that can detect the patterns of metabolites within a sample and retain the spatial information. The patterns of metabolites can provide further insight into the functioning of cells. Our data included mass spectrometry images of excised tissue samples from patients who underwent the removal of skin and breast cancer. By applying computational analysis to a pathologist-defined tumor boundary, we compared patterns of metabolites to the physical annotations. We proposed a linear model that reconstructed mass spectrometry signals from the tumor boundary using only tumor and non-tumor signals. By creating a linear combination of both tumor and non-tumor signals, we could determine the type of metabolite activity surrounding the tumor boundary. We hypothesized a linear relationship between metabolite signals along the tumor border and between tumor and non-tumor regions. Our analysis strongly supported our hypothesis. The results suggested that the tumor stroma was a biologically active zone that extended past the pathologically annotated boundary in every sample.

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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
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This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
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