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A Mixed Integer Linear Programming Optimization Model for Capturing Expert Planning Style in Interstitial Low Dose Rate Prostate Brachytherapy Open Access

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Other title
Subject/Keyword
interstitial prostate brachytherapy
capture
linear programming
automated treatment plans
manual treatment plans
large scale optimization model
expert planning style
radiation therapy
mimic
simplex algorithm
treatment planning system
mixed integer programming
low dose rate (LDR)
clinical assessment
branch and bound algorithm
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Babadagli, Mustafa E
Supervisor and department
Doucette, John (Mechanical Engineering)
Sloboda, Ron (Oncology)
Examining committee member and department
Rouhani, Hossein (Mechanical Engineering)
Askari-Nasab, Hooman (Civil Engineering)
Ingolfsson, Armann (Accounting, Operations and Information Systems)
Department
Department of Mechanical Engineering
Specialization
Engineering Management
Date accepted
2016-12-23T13:26:35Z
Graduation date
2017-06:Spring 2017
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Low dose rate (LDR) brachytherapy is a minimally invasive form of radiation therapy, used to treat prostate cancer, and it involves permanent implantation of radioactive sources (seeds) inside of the prostate gland. Treatment planning in brachytherapy involves a decision making process for the placement of radioactive sources in order to deliver an effective dose of radiation to cancerous tissue in the prostate while sparing the surrounding healthy tissue. Such a decision making process can be modeled as a mixed-integer linear programming (MILP) problem. In this thesis, we initially introduce a novel MILP optimization model for interstitial LDR prostate brachytherapy that attempts to mimic the qualities of treatment plans produced manually by expert planners. Our approach involves incorporating a unique set of clinically important constraints, called spatial constraints, into our models. These constraints are combined with several proposed data processing techniques, and collectively these methods enable us to capture the fundamental aspects of the planning style present at a local cancer clinic. While this represents the primary clinical challenge to overcome in this thesis, the engineering challenge involves producing clinically acceptable treatment plans in a matter of seconds to minutes with our automated planning system. Such a goal initially presents itself to be a difficult one to accomplish, as our large-scale models exhibit tendencies of intractability while using high-resolution data sets. We introduce pseudo high-resolution data sets and constraint-violating feasibility-based modelling in order to overcome these issues related to solution performance of our model. Through the synergistic effects of these two modelling techniques, we demonstrate the ability to produce treatment plans with solution times suitable for pre-operative and intra-operative planning, which constitute the two main forms of treatment planning in LDR brachytherapy. Specifically speaking, our solution times range from less than a minute to roughly five minutes for prostates of varying shapes and sizes (volume-wise, the tested prostates range from 20.4 cc to 63.1 cc). Finally, with the help of a radiation oncologist, we verify the clinical acceptability of our automated plans through a pilot study involving data from twenty patients. Therefore, through this study we also confirm the clinical suitability of the several concepts introduced in this thesis; namely, these are spatial constraints, pseudo high-resolution data sets and constraint-violating feasibility-based modelling. Following these results, we consequently identify three main areas of improvements for our automated system: avoiding placement of strands outside of the prostate and seeds in the bladder, as well as lowering dose to the organs-at-risk. Moving forward, we suggest branching out and examining the clinical performance of our automated planning system in intra-operative planning, as the results obtained in thesis reflect the performance of our approach within pre-operative planning. In summary, through this thesis we demonstrate the ability to uniquely capture the planning style of expert planners at a local clinic in a fraction of the time that it currently takes expert planners to produce treatment plans.
Language
English
DOI
doi:10.7939/R3GX4564Q
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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