Brenner, Darren Michael RIehlHeitman, Steven JamesMazurek, Matthew2023-09-272023-09-272023-09-22Mazurek, M. (2023). Sessile serrated lesions in focus: examining temporal trends, patient risk factors, and the role of the endoscopist in lesion detection (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/117221https://doi.org/10.11575/PRISM/42063Serrated polyps of the colorectum have become increasingly recognized as an important clinical entity, as these precursor lesions are hypothesized to be responsible for up to 25% of sporadic cases of colorectal cancer. Much confusion exists regarding these polyps; particularly, their classification and associated malignant risk due to varied nomenclature, evolving pathological criteria, and ongoing research in prognostication. A specific subtype, sessile serrated lesions (SSLs), are of particular interest, as they are the most prevalent premalignant subtype and are over-represented in cases of interval cancers. Accurate identification and risk assessment remains a challenge owing to variable detection of clinically relevant serrated lesions by endoscopists, high inter-observer variability in diagnosis by pathologists, and an incomplete understanding of risk of future neoplasia. In this thesis, we analyze over 75,000 screening colonoscopies performed over a five-year period at a dedicated, large volume, high-efficiency screening centre to identify trends in the endoscopic detection of SSLs. The intent of this work is to better understand the temporal factors influencing SSL detection prevalence, the patient risk factors that are associated with these lesions, and how detection is related to procedural and endoscopist factors. The analysis includes consideration of traditional statistical methods as well as novel machine learning algorithms. We demonstrated a positive temporal trend in SSL detection over study period and identified several patient, procedural, and endoscopist factors associated with SSL detection. Machine learning models improved upon the predictive capabilities of traditional statistical models, yet a significant proportion of variability in risk remained unexplained, underscoring the complexity of accurately predicting SSLs. Endoscopic detection of SSLs demonstrates strong correlation with other detection metrics, notably adenoma detection rate, implying a shared underlying skillset requisite for the identification of these distinct polyp types. This connection highlights opportunities for enhancing detection through benchmarking and established quality improvement strategies.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Colorectal cancer screeningColonoscopySessile serrated lesionsMachine learning modelsMedicine and SurgeryEpidemiologyPublic Healthmaster thesis