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How Much does Patient Repositioning using On-Treatment-Table
CT Imaging Improve Prostate Cancer Treatment Outcome?
Mark S. Foskey, Julian G. Rosenman, Lav K. Goyal, Daniel B. Fried, Elaine M. Zeman, Jun Lian, Sarang C. Joshi
Department of Radiation Oncology, University of North Carolina
Introduction
Idea: Simulate dose delivery with and without image
guided radiotherapy (IGRT) based on CT data from
actual patients
Treatment procedure
• Currently being used in clinic
• Patient scanned with in-room CT (CT-on-rails), with
fiducial markers
• Image initially aligned to planning image by markers
• Then manually aligned, prostate-to-prostate
• Table is shifted (no rotation)
Methods
• 29 anonymous image sets with 538 treatment
images
• Number of images per patient varied from 3 to 38
• Scanned before treatment on a Siemens Primatom
scanner, resolution 0.098 × 0.098 × 0.3 cm.
• Simple, very tight standardized treatment plan
created for simulation
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Treatment Plan Criteria
CTV: the prostate + 5 mm margin, excluding the rectum
PTV: prostate + 1 cm margin, excluding rectum
Beam Angles: AP, left and right at 40° above horizontal,
left and right at 10° below horizontal
Dose: 78 Gy in 2Gy fractions
Optimization targets:
• Beams outline conforms to PTV, optimized to target
dose of 78 Gy on all of CTV
• Rectum: PV65 < 17%, PV40 < 35% (PVn = percent of
volume receiving at least n Gy.)
Display from treatment planning system showing
beam angles and relevant anatomical structures
Dose Calculation
• Calculate a dose distribution for each day
• Registrations were checked visually on an axial and
sagittal slice through the prostate
• Apply fluid registration (Joshi ’00) to deform each
image into correspondence with the planning image
• Apply resulting deformation to the daily dose
distribution
• Sum dose at each voxel
Metrics Evaluated
• For CTV and prostate, equivalent uniform dose
(EUD) calculated using the generalized Niemierko
formula with exponent a  10 .
• EUD calculated both for accumulated dose and for
each day
• For the rectum, the volumes receiving more than 40
and 65 Gy were calculated
Planned dose, dose simulated without correction, and dose simulated with correction. Cases are sorted by
uncorrected dose. The number of images used for each case is shown along the horizontal axis.
Results
Rectum
CTV:
• 13 patients saw an improvement in EUD of at least
2 Gy
• 2 patients saw a worsening of the dose distribution
• Median improvement 1.62 Gy (p < .0001)
Prostate:
• 1 patient improved by at least 2 Gy
• 21 saw some improvement
• 7 saw a worsening
• Median improvement .33 Gy (p < .0001)
• With IGRT, rectal dose tended to be closer to the
plan than without
• True in 19 out of 29 cases for percentage of
volume receiving 40 Gy (PV40), 17/29 for PV65
Differences between proximity to planned rectal
dose values with and without IGRT. Positive values
mean dose is closer to plan with IGRT than without
Histograms of EUD improvement for the prostate and CTV.
• Without IGRT, rectal dose tended to be less than in
the plan (20 of 29 cases for PV40, 23 for PV65)
• No consistent direction of change between IGRT and
conventional treatment
• Results of Fowler ’05 suggest a 2-Gy improvement
could lead to a 5% increase in biochemical relapsefree survival
EUD improvement achieved (Gy)
Relationship of Improvement to Initial Deficit
• Improvement in the CTV EUD resulting from IGRT
correlates well with the severity of the initial
undertreatment (ρ=.74)
Histogram of differences between rectal dose with and
without IGRT. Positive values mean IGRT dose is greater
• Fewer patients exceeded prescribed thresholds with
IGRT than without (2 vs. 6 for PV40, 0 vs. 2 for PV65)
Conclusion
EUD improvement needed (Gy)
Improvement due to correction, plotted against the
improvement that would be needed to match planned EUD.
• With tight margins, IGRT, can provide substantial
improvement in delivered dose
• Dose improvement suggests improvement in bRFS of
5% or more for some cases
• IGRT seems to improve correspondence between
predicted and delivered rectal dose
Supported by DOD Program DAMD17-03-1-0134, NIH Grant 5 R01 RR01861503, and Siemens Corporation