Characterization of the tumor microenvironment in colorectal cancer using digital pathology and AI - Tumor regression grading and biomarkers of treatment response and survival
PhD defence by Dea Natalie Munch Jepsen
Assessment Committee
Professor Eric Santoni-Rugiu, Department of Clinical Medicine, University of Copenhagen (Chairperson)
Associate professor Jan Lindebjerg, Department of Regional Health Research, University of Southern Denmark and Department of Pathology, Vejle Hospital
Associate professor Pamela Buchwald, Department of Clinical Sciences, Lund University, Malmö, Sweden.
Supervisors
Clinical Research Associate Professor Anne-Marie Kanstrup Fiehn
Clinical Professor Ismail Gögenur
PhD, Molecular Biologist Jens Ole Eriksen
Department
Department of Clinical Medicine
Graduate Programme
Clinical Cancer Research
Place
Building: Zealand University Hospital, Roskilde, Room: Lille Auditorium, Vestermarksvej 9, 4000 Roskilde
Email address to gain access to the thesis: dnj@regionsjaelland.dk.
You will either receive a copy of the thesis or be informed where you can read a physical copy.
Recipients of copies of the thesis are not allowed to share or distribute it due to copyright compliance.
Short description of the thesis
In colorectal cancer (CRC), tissue biomarkers can support precision-based approaches and guide treatment decisions. This thesis investigated the tumor microenvironment in CRC, aiming to identify and characterize potential biomarkers of pathological treatment response following neoadjuvant therapy and survival. The thesis comprises four retrospective studies of patients with surgically resected CRC. The studies employed tissue-based analyses using immunohistochemistry and digital pathology, including artificial intelligence-based methods, to assess and quantify tumor regression, tumor-infiltrating lymphocytes, and PD-L1 expression. Collectively, these studies contribute to the current knowledge of predictive and prognostic biomarkers in CRC and provide insights into the potential application of digital pathology for characterizing the tumor microenvironment.