Abstract
Tertiary Lymphoid Structures (TLS) are organised immune configurations that arise in non-immune tissue as a result of chronic inflammation. TLS typically resemble the structure and cellular composition of secondary follicles found in lymph nodes and mimic their functionality by acting as a site for antigen presentation, immune cell maturation, and antibody production. Chronic inflammation leading to TLS formation can arise from autoimmune diseases such as rheumatoid arthritis, Crohn’s disease, and type 1 diabetes. Increasing evidence has implicated roles for TLS in some cancers, showing they may be of use as a new prognostic and predictive biomarker. Despite this, TLS have been inconsistently defined which has led to some contradictory results, particularly as a result of variability in both detection methodology and patient stratification strategies. This thesis aims to develop and apply a consistent definition of TLS in a single medium that can be automated for robust and reproducible detection and annotation in digital H&E-stained slides. Additionally, an artificial intelligence (AI) model underpinned by novel software contributions, has been developed for automated TLS detection.Thesis is embargoed until 31st December 2027.
| Date of Award | Dec 2024 |
|---|---|
| Original language | English |
| Awarding Institution |
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| Sponsors | Northern Ireland Biobank |
| Supervisor | Stephanie Craig (Supervisor), Richard Gault (Supervisor) & Jacqueline James (Supervisor) |
Keywords
- tertiary lymphoid structures
- artificial intelligence
- deep learning
- machine learning
- pathology
- digital pathology
- cancer