Author ORCID Identifier
0000-0003-1583-7121
Date of Graduation
5-2021
Document Type
Dissertation (PhD)
Program Affiliation
Medical Physics
Degree Name
Doctor of Philosophy (PhD)
Advisor/Committee Chair
Laurence Court
Committee Member
Peter Balter
Committee Member
Carlos Cardenas
Committee Member
Caroline Chung
Committee Member
Rebecca Howell
Committee Member
Ann Klopp
Committee Member
Christine Peterson
Abstract
Palliative radiotherapy is an effective treatment for the palliation of symptoms caused by vertebral metastases. Visible evidence of disease is localized on medical images as part of the treatment planning process. However, complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with wrong level treatments of the spine. In addition, erroneous manual contouring of anatomic structures is a major failure mode in radiotherapy treatment planning.
The purpose of this study is to mitigate the challenges associated with treatment planning of the spine by automating the treatment planning process for three-dimensional conformal radiotherapy. To accomplish this, deep and machine learning models will work in symphony within a multi-stage framework to perform image-based tasks in place of manual tasks. An automated solution that is efficient, effective, and safe would be especially valuable for clinics seeking to expedite their palliative radiotherapy planning services or optimize their use of diagnostic and simulation CT imaging for radiotherapy treatment planning.
The central hypothesis of this work is that that 90% of automated treatment plans for bony metastases of the spine are clinically acceptable and can be generated in less than 10 minutes. Additionally, that potential mistreatment can be flagged with 100% sensitivity and at least 75% specificity.
Keywords
segmentation, treatment planning, deep learning, palliative radiotherapy, spinal metastases
Included in
Data Science Commons, Other Computer Engineering Commons, Other Physics Commons, Palliative Care Commons, Translational Medical Research Commons