POINTÕCOUNTERPOINT
Suggestions for topics suitable for these Point/Counterpoint debates should be addressed to the Moderator: William R.
Hendee, Medical College of Wisconsin, Milwaukee: [email protected]. Persons participating in Point/Counterpoint
discussions are selected for their knowledge and communicative skill. Their positions for or against a proposition may
or may not reflect their personal opinions or the positions of their employers.
D m rather than D w should be used in Monte Carlo treatment planning
H. Helen Liu
The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston,
Texas 77030
(Tel: 713-745-4502, E-mail: [email protected])
Paul Keall
Medical College of Virginia, Radiation Physics Section, Department of Radiation Oncology,
Richmond, Virginia 23298
(Tel: 804-628-0980, E-mail: [email protected])
William R. Hendee, Moderator
!Received 30 January 2002; accepted for publication 30 January 2002; published 24 April 2002"
#DOI: 10.1118/1.1473137$
OVERVIEW
D w , the absorbed dose to water, has traditionally been the
normalizing factor for dose computations related to treatment
planning in radiation therapy with high-energy x-rays. This
normalizing factor relates the treatment plan to the x-ray
calibration process described in TG 21 or 51. However, treatment planning employing Monte Carlo techniques allows the
expression of radiation transport and energy deposition in
patient representative media. The dose reported in this process can be either the dose-to-medium, D m , or the dose-towater, D w , calculated with stopping power ratios. Many
physicists believe that D m is the preferred variable for treatment planning, and that it should replace D w in this capacity.
The controversy of D m versus D w is the topic of this month’s
Point/Counterpoint.
Arguing for the Proposition is
H. Helen Liu, Ph.D. Dr. Liu
worked on radiation dosimetry
and Monte Carlo simulation
for her Ph.D. research in the
Division of Radiation Oncology at the Mayo Clinic. She
completed residency training
at the Mayo Clinic upon
completion of her Ph.D. in
1997. She is now an Assistant
Professor in the Department of
Radiation Physics at the University of Texas MD Anderson Cancer Center. Her research
interests include Monte Carlo simulation, treatment planning
optimization, and biophysical models for measuring radiation response in radiation therapy.
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Arguing against the Proposition is Paul Keall, Ph.D. Dr.
Keall is currently an Assistant
Professor in the Department of
Radiation Oncology at the
Medical College of Virginia.
He has been working on
Monte
Carlo
treatment
planning-related research for
over a decade. His Ph.D. dissertation involved the development and evaluation of ‘‘Superposition Monte Carlo,’’ a
dose calculation algorithm combining elements of both the
superposition/convolution and Monte Carlo algorithms. Paul
has acted as a consultant to the IAEA on Monte Carlo Transport in Radiotherapy. His current Monte Carlo research interests include hip prosthesis calculations, IMRT calculations, EPID dosimetry and large-scale comparisons between
Monte Carlo and other algorithms.
FOR THE PROPOSITION: H. Helen Liu, Ph.D.
Opening Statement
The ability to compute the actual dose to medium !D m " is
a unique and advantageous feature of Monte Carlo simulation for radiotherapy treatment planning. The rationale of
converting D m back to the dose to water D w is driven solely
by the desire to comply with tradition. D w has been used in
treatment planning because accurate heterogeneity correction
methods were not readily available. As Monte Carlo treatment planning emerges, new standards of practice will be
established to reflect the advances that Monte Carlo techniques will bring. The motivation for using Monte Carlo
simulation in treatment planning is essentially to achieve the
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© 2002 Am. Assoc. Phys. Med.
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Liu and Keall: PointÕCounterpoint
greatest accuracy in dose calculation. Converting D m back to
D w requires computing stopping power ratios for local voxels, a process that adds uncertainty in the calculations and
makes Monte Carlo simulation more time consuming and
complicated. This conversion defeats the purpose of using
Monte Carlo simulation.
The clinical impact of switching from D w to D m is not
expected to be significant, mainly because most tissues of
interest in radiotherapy are similar to water. Thus, the difference between D m and D w will not change clinical outcomes
to a noticeable degree, particularly since the uncertainty of
clinical results is several orders of magnitude greater. With
respect to radiobiological effects, there is no reason that D m
cannot be used in place of D w for purposes of linking dose to
biological response. In fact, the energy deposited in individual voxels is related more directly to D m than to D w .
Insofar as the dosimetry calibration protocol is concerned,
the use of D m in treatment planning will not affect the calibration of D w recommended by national and international
protocols. This is because in Monte Carlo simulation, the
relationship is known between dose !either D w or D m " and
the required number of photon histories to be simulated.
From a prescription of D m in a patient, the corresponding
number of photon histories can be converted to monitor units
!MUs" through use of the calibrated value of D w per MU. In
other words, the calculation of MU can proceed in the same
way for either D w or D m in Monte Carlo simulation.
In summary, the advantages of Monte Carlo planning systems are improved accuracy in dose calculations and the possibility of obtaining D m directly for various tissues. Converting D m to D w involves additional complications and adds
possible sources of error for Monte Carlo calculations. D m
should be used in Monte Carlo planning, and will not have a
significant impact on current clinical practice. Instead, use of
D m allows Monte Carlo planning to establish more accurate
dose delivery, and to provide a closer relationship between
tissue response and dose.
Rebuttal
Dr. Keall raises some important issues concerning the use
of Monte Carlo treatment planning in routine clinical practice. Included in his concerns is a preference for D w rather
than D m in Monte Carlo treatment planning.
One solution to Dr. Keall point !3" on specification of the
medium is to standardize the conversion of CT numbers to
radiological properties of the medium in a consistent and
uniform manner for different Monte Carlo treatment planning systems. I suspect that this effect may not be clinically
significant because D w and D m are quite similar for most
biological tissues. Nevertheless, the subject warrants further
investigation.
With respect to the dosimetry calibration protocol, the relationship between D w obtained in a reference calibration
condition, and D m for a patient prescription, is known from
Monte Carlo simulation. Hence, the monitor unit calculation
in Monte Carlo treatment planning is straightforward, and
does not affect implementation of standard dosimetry protoMedical Physics, Vol. 29, No. 5, May 2002
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cols to any degree. For conventional treatment planning,
many institutions use dose to muscle rather than dose to
water for monitor unit calculation. The ratio between the two
is well-known and has been used to scale monitor units in
clinical practice.
Obstacles to using D m rather than D w for Monte Carlo
treatment planning are pragmatic in nature, including how to
conform with convention and incorporate past clinical experience. The relationship between D w and D m can be computed, and dose response data for tumor and normal tissues
can be easily scaled in order to use D m for treatment planning purposes. In fact, for cells imbedded in heterogeneous
tissues such as lung or bone, dose to the cells is not reflected
accurately by either D w or D m . This is because the CT imaging resolution is not sufficient to detect subvoxel structures, and D w or D m simply represents an averaged dose
value in the voxel. In this case, either D w or D m can be used
to indicate the energy delivered to the cells and the subsequent radiobiological effects.
The use of D m in Monte Carlo treatment planning is a
natural and suitable approach to avoiding the additional complexity and uncertainty of converting D m to D w . New standards of practice using D m should be implemented to provide a smooth transition from conventional to Monte Carlo
treatment planning.
AGAINST THE PROPOSITION: Paul Keall, Ph.D.
Opening Statement
I aim to convince you that prescribing, evaluating and
reporting dose-to-water !D w " rather than dose-to-medium
!D m " for Monte Carlo treatment planning is both obvious
and necessary for the following reasons: !1" Clinical experience is D w -based, !2" Dosimetry protocols are D w -based, !3"
The ‘‘medium’’ to report dose in is always a guess, and !4"
D w -based IMRT allows us to achieve the clinical prescription.
1. Clinical experience is D w -based. Since the introduction
of computer-based treatment planning in the 1960s, dose calculation algorithms have assumed that the patient is composed of waterlike composition of varying density. All
manual calculations assume waterlike composition. This assumption is reasonable, as water makes up the bulk of our
body cells and tissues. All clinical experience, and doses
reported in the multitude of clinical trials, both past and ongoing, are with respect to D w .
2. Dosimetry protocols are D w -based. As stated in the
overview, modern dosimetry protocols !e.g., IAEA, AAPM"
are based on D w . Also, the factors that convert ionizationin-air to D w for the calibration have been determined or verified by Monte Carlo calculations. It seems reasonable that
the reported dose for treatment planning is directly traceable
to the calibration, and thus the reported dose should be D w .
3. The ‘‘medium’’ to report dose in is always a guess. In
Monte Carlo treatment planning, the CT numbers are converted not only to densities, but also to media. These media
are generally obtained from ICRU or ICRP publications.
However, who knows whether a patient’s organ, e.g., a liver,
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Liu and Keall: PointÕCounterpoint
has exactly the same composition as the ‘standard’’ liver?
Furthermore, the same CT number can be obtained from a
higher density/lower Z medium !e.g., soft tissue" and from a
lower density/higher Z medium !e.g., lung". Thus, in the absence of 3D body composition analysis accompanying the
CT scan, we can only guess at patient composition. Errors in
specifying the medium for the dose calculation will always
be present. Thus, reporting D m when we do not know what
the medium is seems somewhat illogical.
4. D w -based IMRT allows us to achieve the clinical prescription. The prescription dose is determined with the aim
that the tumor cells receive a lethal dose, whilst the normal
cells embedded in the tumor receive a dose they can recover
from. Take, for example, a head and neck cancer in which
the tumor infiltrates the mandible. If optimized using D m ,
the dose-to-mandible will be equal to the prescription dose,
say 72 Gy. However, the dose-to-osteocytes !being waterlike" within the mandibular bony matrix will be close to 80
Gy !assuming a Bragg-Gray cavity". Hence there will be a
higher chance of bone weakening, fracture and necrosis than
at 72 Gy. If optimized using D w , we can modulate the intensity and thus dose so that the dose-to-tumor !being waterlike" and dose-to-osteocytes within the mandible receive the
prescription dose, 72 Gy. The point here is that D w , rather
than D m , is the desired quantity from a clinical perspective,
and IMRT with D w Monte Carlo can closely approach the
desired prescription.
From the four reasons mentioned above, I conclude that
we should use D w rather than D m for Monte Carlo treatment
planning !as we routinely use in our clinic for our IMRT
patients". Furthermore I would advise both commercial and
academic Monte Carlo developers to report D w rather than
Dm .
Rebuttal
Dr. Liu presents arguments that converting D m to D w introduces !i" additional complexity and !ii" additional uncertainty.
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On the issue of increased complexity. Converting D m to
D w does involve additional computations. There is a small
computational overhead associated with scoring D w rather
than D m . However, we must ask ourselves why we want
Monte Carlo in the first place? If complexity was a limitation, we would still be performing 2D treatment planning. If
we make the effort to implement Monte Carlo treatment
planning, let us do it properly.
On the issue of uncertainty. The additional uncertainty
that Dr. Liu is alluding to is the uncertainty of the stopping
power ratios of water-to-medium. By virtue of taking a ratio,
systematic uncertainties in the stopping powers are reduced.
Furthermore, the stopping power values for water are more
accurate than those for many other tissues. There are two
uncertainties, however, that exceed those of stopping power
values. These are !i" the CT voxel composition uncertainty
and !ii" institutional variations.
!i" A 1!1!3 mm3 CT voxel can contain up to 300 thousand cells of differing types and composition. Even the most
accurate Monte Carlo is limited by the CT resolution. When
we talk about scoring D m to a voxel, which cell type do we
mean? Assuming cells are Bragg-Gray cavities, each cell
type within the CT voxel will have a different D m . However,
when we score D w , Bragg–Gray theory tells us that the dose
to each cell within the CT voxel is the same, thus eliminating
intra-CT voxel variations.
!ii" Different institutions and Monte Carlo developers/
vendors use different material types and density cut offs. For
example, the default number of patient representative media
in DOSXYZ !a widely used EGS4 Monte Carlo user code" is
four. However, advanced users are including either quasicontinuous or continuous media assignments. Thus, D m will be
site dependent. At least, by virtue of Bragg–Gray theory, if
D w is used institutional variations will be reduced.
Dr. Liu also states that the rationale of converting D m to
D w is driven simply by the need to comply with convention. I
completely agree. As with dose calibration, for clinical trial
results to be meaningful, the dose reported by different institutions should have a consistent convention.