2026-10-07T15:04:51Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32640339 2026-10-01T16:32:03Z portal_1172 item_type_8 month_year_10_2026

The prognostic value of tertiary lymphoid structures in cancer; a digital pathology and artificial intelligence approach Kristopher David McCombe (24169203) PUREID: 616555227 tertiary lymphoid structures artificial intelligence deep learning machine learning pathology digital pathology cancer 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.<br><br><i>Thesis is embargoed until 31st December 2027</i>.<br><br><br> 2026-10-01T16:32:03Z Text Thesis 10.17034/32640339.v1 https://figshare.com/articles/thesis/The_prognostic_value_of_tertiary_lymphoid_structures_in_cancer_a_digital_pathology_and_artificial_intelligence_approach/32640339 All Rights Reserved Open Access after 2027-12-31