Knowledge-based IMRT treatment planning for prostate cancer.

dc.contributor.advisor

Lo, Joseph Y

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Das, Shiva K

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Tourassi, Georgia

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Turkington, Timothy

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Chanyavanich, Vorakarn

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United States

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2011-05-20T19:35:48Z

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2011

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Medical Physics

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The goal of intensity-modulated radiation therapy (IMRT) treatment plan optimization is to produce a cumulative dose distribution that satisfies both the dose prescription and the normal tissue dose constraints. The typical manual treatment planning process is iterative, time consuming, and highly dependent on the skill and experience of the planner. We have addressed this problem by developing a knowledge based approach that utilizes a database of prior plans to leverage the planning expertise of physicians and physicists at our institution. We developed a case-similarity algorithm that uses mutual information to identify a similar matched case for a given query case, and various treatment parameters from the matched case are then adapted to derive new treatment plans that are patient specific. We used 10 randomly selected cases matched against a knowledge base of 100 cases to demonstrate that new, clinically acceptable IMRT treatment plans can be developed. This approach substantially reduced planning time by skipping all but the last few iterations of the optimization process. Additionally, we established a simple metric based on the areas under the curve (AUC) of the dose volume histogram (DVH), specifically for the planning target volume (PTV), rectum, and bladder. This plan quality metric was used to successfully rank order the plan quality of a collection of knowledgebased plans. Further, we used 100 pre-optimized plans (20 query x 5 matches) to show that the average normalized MI score can be used as a surrogate of overall plan quality. Plans of lower pre-optimized plan quality tended to improve substantially after optimization, though its final plan quality did not improve to the same level as a plan that has a higher pre-optimized plan quality to begin with. Optimization usually improved PTV coverage slightly while providing substantial dose sparing for both bladder and rectum of 12.4% and 9.1% respectively. Lastly, we developed new treatment plans for cases selected from an outside institution matched against our sitespecific database. The knowledge-based plans are very comparable to the original manual plan, providing adequate PTV coverage as well as substantial improvement in dose sparing to the rectum and bladder. In conclusion, we found that a site-specific database of prior plans can be effectively used to design new treatment plans for our own institution as well as outside cases. Specifically, knowledge-based plans can provide clinically acceptable planning target volume coverage and clinically acceptable dose sparing to the rectum and bladder. This approach has been demonstrated to improve the efficiency of the treatment planning process, and may potentially improve the quality of patient care by enabling more consistent treatment planning across institutions.

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https://hdl.handle.net/10161/3879

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eng

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Artificial Intelligence

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Decision Support Systems, Clinical

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Humans

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Knowledge Bases

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Male

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Prostatic Neoplasms

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Radiotherapy Planning, Computer-Assisted

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Radiotherapy, Conformal

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Retrospective Studies

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Therapy, Computer-Assisted

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Treatment Outcome

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Knowledge-based IMRT treatment planning for prostate cancer.

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Dissertation

duke.embargo.months

6

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Duke

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Duke

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Pratt School of Engineering

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Duke

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Pratt School of Engineering

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Biomedical Engineering

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Duke

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School of Medicine

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Duke

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School of Medicine

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Clinical Science Departments

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Duke

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School of Medicine

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Clinical Science Departments

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Radiation Oncology

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Duke

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School of Medicine

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Clinical Science Departments

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Radiology

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Duke

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School of Medicine

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Clinical Science Departments

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Surgery

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Duke

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School of Medicine

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Clinical Science Departments

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Surgery

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Surgery, Urology

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Duke

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School of Medicine

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Institutes and Centers

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Duke

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School of Medicine

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Institutes and Centers

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Duke Cancer Institute

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