Development of a prognostication model for breast cancer in British Columbia
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Epp, Joyce
issued: 2020-07-13T20:27:10Z
issued: 2020
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Detecting breast cancer in its early stages improves patient outcomes, as the disease is often more treatable at early stages. In British Columbia, BC Cancer Breast Screening provides screening mammography as the primary means of early detection. As with any screening test, false positive results do occur. However, false positive results from screening mammography have been shown to be associated with the risk of a future breast cancer. Moreover, the mammographic features that indicate a mammogram is abnormal have been shown to stratify the increase in risk. Models to predict breast cancer risk currently have a modest ability to distinguish between who will be diagnosed and who will not, with concordances in the 0.55 to 0.71 range. Using risk factor, screening, and diagnosis data from BC Cancer Breast Screening, we build several models that incorporate mammographic features with the aim of creating a risk prediction model that provides a better risk prediction than current models. We fit a Cox proportional hazards model, a time varying Cox model, an accelerated failure time model and a Poisson regression model. We found that mammographic features are associated with an increased risk of breast cancer and increased model concordance by about 0.006. However, while the internal calibration was satisfactory, as it was close to 1, the discrimination of the models was unsatisfactory, with concordances around 0.63. Compared to several models in the literature, our model performance was similar, with concordances near 0.63 and internal calibration close to 1 for both ours and the published models examined. We conclude that breast cancer risk prediction is not substantially improved by the incorporation of mammographic features in a categorical format.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/75136
note: Development of a prognosticationmodel for breast cancerin British ColumbiabyJoyce EppB.Sc., The University of British Columbia, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)July 2020c© Joyce Epp, 2020The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Development of a prognostication model for breast cancerin British Columbiasubmitted by Joyce Epp in partial fulfilment of the requirements of thedegree of Master of ScienceDr. Jeff Andrews, I. K. Barber School of Arts & SciencesSupervisorDr. Rasika Rajapakshe, I. K. Barber School of Arts & SciencesCo-supervisorDr. John Braun, I. K. Barber School of Arts & SciencesSupervisory Committee MemberDr. Ramon Lawrence, I. K. Barber School of Arts & SciencesSupervisory Committee MemberDr. Abbas Milani, School of EngineeringUniversity ExamineriiAbstractDetecting breast cancer in its early stages improves patient outcomes, asthe disease is often more treatable at early stages. In British Columbia, BCCancer Breast Screening provides screening mammography as the primarymeans of early detection. As with any screening test, false positive resultsdo occur. However, false positive results from screening mammography havebeen shown to be associated with the risk of a future breast cancer. More-over, the mammographic features that indicate a mammogram is abnormalhave been shown to stratify the increase in risk. Models to predict breastcancer risk currently have a modest ability to distinguish between who willbe diagnosed and who will not, with concordances in the 0.55 to 0.71 range.Using risk factor, screening, and diagnosis data from BC Cancer BreastScreening, we build several models that incorporate mammographic featureswith the aim of creating a risk prediction model that provides a better ri…
has type: Thesis/Dissertation
date issued: 2020-09
is shown at: 10.14288/1.0392350
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/75136