Date of Submission

8-2024

Document Type

Thesis

Degree Name

Master of Science in Industrial Engineering

Department

Mechanical and Industrial Engineering

Advisor

Marzieh Soltanolkottabi, Ph.D.

Committee Member

M.Ali Montazer, Ph.D.

Committee Member

Narjes Sadeghiamirshahidi, Ph.D.

Committee Member

Adwoa Donyina, Ph.D.

Keywords

Lung Cancer, Biomarker, Whole Genome Sequencing (WGS), Diagnostic Process

MeSH

Lung Neoplasms, Biomarkers, Whole Genome Sequencing

LCSH

Lungs--Cancer--Diagnosis, Biochemical markers

Abstract

Cancer, particularly lung cancer, presents significant diagnostic and economic challenges globally. Timely diagnosis and cost management play pivotal roles in treatment success. A biomarker is any measurable molecule in blood, bodily fluids, or tissues, indicating the potential presence of an abnormal bodily process, condition, or disease. Biomarker testing is a laboratory test in oncology that is used in the selection of targeted cancer treatments and to help avoid ineffective treatments. Whole Genome Sequencing (WGS), is a biomarker test which while more comprehensive, comes at a higher cost. This study proposes an agent-based simulation model within a game-theoretic framework to examine the benefits of prioritizing WGS in the diagnostic process for lung cancer across various hospital settings. The typical diagnostic pathway for lung cancer, involving tests like PD-L1, ALK, EGFR, KRAS, BRAF, and ROS1, can lead to time-consuming referrals and potentially higher costs due to unsuccessful standard of care (SOC) tests. This thesis scrutinizes these pathways and evaluates the impacts of different referral scenarios on the number of diagnosed patients and treatment costs, drawing on a system dynamics model. Initial findings suggest that uniform referral strategies are not universally optimal and can result in delayed WGS testing results. Aiming to establish a refined strategy for each hospital type and determine the optimal timing for WGS, an agent-based simulation is proposed to emulate the diagnostic journey. The model considers costs, success rates, and diagnostic timeframes, while a game theoretic approach assesses each hospital's decision-making regarding WGS access. The goal is to facilitate the earliest and most cost-effective treatment for patients. Objectives of this thesis include developing an agent-based model to mirror current pathways, evaluating referral scenarios, formulating a game-theoretic analysis, and understanding the sensitivity of various parameters crucial to decision-making. The research will culminate in actionable insights for healthcare providers and policymakers, specifically in enhancing WGS utilization in lung cancer diagnosis and treatment.

Anticipated outcomes include a versatile model for strategizing referral practices, yielding an optimal approach for a given health center constellation. The broader implications of this work extend beyond lung cancer, offering a template for enhancing diagnostic efficiency in various cancers and other sectors where strategic decisions regarding shared resources are vital. The study stands to influence patient care positively by improving diagnostic accuracy and economic efficiency in healthcare delivery.

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