Knowledge-based IMRT treatment planning for prostate cancer.
Date
2011
Author
Advisors
Lo, Joseph Y
Das, Shiva K
Tourassi, Georgia
Turkington, Timothy
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Abstract
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.
Type
DissertationDepartment
Medical PhysicsSubject
Artificial IntelligenceDecision Support Systems, Clinical
Humans
Knowledge Bases
Male
Prostatic Neoplasms
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Conformal
Retrospective Studies
Therapy, Computer-Assisted
Treatment Outcome
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https://hdl.handle.net/10161/3879Citation
Chanyavanich, Vorakarn (2011). Knowledge-based IMRT treatment planning for prostate cancer. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/3879.Collections
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