Causal machine learning to optimize treatment decisions for patients with endometrial cancer
department or school: Medicine, Faculty of
department or school: Obstetrics and Gynaecology, Department of
data provider: DSpace
degree campus: UBCV
creator: Johal, Parveen
issued: 2025-01-31T08:00:00Z
issued: 2022
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Background: In endometrial cancer (EC), histopathological parameters, often unreliably assessed from diagnostic sampling, are used to guide surgical decisions. Surgical decisions, like lymph node (LN) assessment, have implications for post-operative adjuvant therapy decisions, which often vary based on nodal status. The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) subtypes are prognostically distinct, can be assigned on biopsy and have demonstrated exceptional reproducibility. Thus, they may be better suited to guide decision-making pre- and post-hysterectomy. As per the current literature, it is not yet well understood how ProMisE can impact decision-making. Objectives: This thesis explores ProMisE in the context of other pre-operative prognostic features that may influence nodal assessment and adjuvant therapy decision-making, measures the concordance of histopathological and molecular features available pre- and post-operatively, explores how ProMisE subtypes could tailor nodal assessment decisions to minimize the variability in adjuvant therapy decisions, and finally, proposes a model for a dynamic treatment regime (DTR) that simultaneously optimizes pre- and post-operative decision-making in EC. Methods: EC patients who underwent surgery, and have a ProMisE subtype assigned, were identified from two institutions. The dataset was generated using retrospective data from previous studies with additional pre-operative parameters collected via chart reviews. Univariate associations among ProMisE, treatment decisions and prognostic factors were measured. Multivariate analyses were used to assess association of survival with LN testing within ProMisE subgroups. A DTR optimizing nodal assessment and adjuvant therapy was estimated using all the parameters available at the time of each decision. To account for the confounders and mediators in our observational data, a counterfactual framework and dynamic weighted survival modelling was used. Resul…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/81846
note: CAUSAL MACHINE LEARNING TO OPTIMIZE TREATMENT DECISIONS FOR PATIENTS WITH ENDOMETRIAL CANCER by PARVEEN JOHAL B.Sc., The University of British Columbia, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Reproductive and Developmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2022 © PARVEEN JOHAL, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Causal Machine Learning to Optimize Treatment Decisions for Patients with Endometrial Cancer submitted by Parveen Johal in partial fulfillment of the requirements for the degree of Master of Science in Reproductive and Developmental Sciences Examining Committee: Dr. Aline Talhouk, Assistant Professor, Obstetrics and Gynecology, UBC Supervisor Dr. Mathias Lécuyer, Assistant Professor, Computer Science, UBC Supervisory Committee Member Dr. Tim Rowe, Associate Professor Emeritus, Obstetrics and Gynecology, UBC Supervisory Committee Member Dr. Janice Kwon, Professor, Obstetrics and Gynecology, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Jessica McAlpine, Professor, Obstetrics and Gynecology, UBC Supervisory Committee Member Dr. Mohamed Bedaiwy, Professor, Obstetrics and Gynecology, UBC Supervisory Committee Member iii Abstract Background: In endometrial cancer (EC), histopathological parameters, often unreliably assessed from diagnostic sampling, are used to guide surgical decisions. Surgical decisions, like lymph node (LN) assessment, have implications for post-operative adjuvant therapy decisions, which often vary based on nodal status. The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) subtypes are prognostically distinct, can be assigned on biopsy and have demonstrated exceptional reproducibility. Thus, they may be better suited to guide de…
has type: Thesis/Dissertation
date issued: 2022-11
is shown at: 10.14288/1.0415837
language: eng
degree discipline: Reproductive and Developmental Sciences
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
contributor: Talhouk, Aline
type: Text
identifier URI: http://hdl.handle.net/2429/81846