A Virtual Clinical Trial to Assess the Impact of Dual-Energy CT on Plan Quality, Dose-delivery Accuracy, and Simulated Outcomes of Patients Treated With Proton or Photon Therapy
Phase: NA
Conditions: Lung Cancer, Brain Cancer, Head and Neck Cancer
Sponsor: Washington University School of Medicine

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A virtual clinical trial to assess the impact of Dual-Energy CT on plan quality, dose-
delivery accuracy, and simulated outcomes of patients treated with proton or photon 
therapy 
 
Washington University School of Medicine 
Department of Radiation Oncology 
660 South Euclid Avenue, Campus Box 8224 
St. Louis, MO  63110 
 
Protocol #: 201711149 
Version Date: 10/5/22 
 
Principal Investigator: 
Clifford Robinson, M.D. 
Phone: (314) 362-8567 
E-mail: clifford.robinson@wustl.edu   
 
Sub-Investigators 
 
 
Department 
Jeffrey Williamson, Ph.D. 
 
Radiation Oncology 
Tianyu Zhao, Ph.D. 
 
 
Radiation Oncology 
Joseph O’Sullivan, Ph.D. 
 
Electrical Engineering 
 
 
 
 
CONFIDENTIAL 
The information contained in this document is regarded as confidential and, except to the extent necessary to 
obtain informed consent, may not be disclosed to another party unless law or regulations require such 
disclosure.  Persons to whom the information is disclosed must be informed that the information is 
confidential and may not be further disclosed by them 
 
 

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A virtual clinical trial to assess the impact of Dual-Energy CT on plan quality, dose-
delivery accuracy, and simulated outcomes of patients treated with proton or photon 
therapy 
 
Protocol Revision History 
 
Initial Approval Version 
 
 
 
 
 
 
01/10/2018 
Amendment #1 Version 
 
 
 
 
 
 
06/01/2018 
Amendment #2 Version 
 
 
 
 
 
 
01/25/2019 
Amendment #3 Version 
 
 
 
 
 
 
05/01/2019 
Amendment #4 Version 
 
 
 
 
 
 
05/17/2019 
Amendment #5 Version 
 
 
 
 
 
 
12/16/2020 
Amendment #6 Version 
 
 
 
 
 
 
03/31/2021 
Amendment #7 Version 
 
 
 
 
 
 
06/08/2022 
Amendment #8 Version 
 
 
 
 
 
 
10/05/2022 
 
 
 

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A virtual clinical trial to assess the impact of Dual-Energy CT on plan quality, dose-
delivery accuracy, and simulated outcomes of patients treated with proton or photon 
therapy 
 
SCHEMA 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Dual-Energy CT scan 
Eligible Patients 
Individuals ≥18 years old with histologically proven cancer 
requiring radiation therapy to a) the thorax or b) H&N or brain. 
Conventional Treatment 
Planning with Uncertainty 
Margin (to be used for therapy) 
Treatment Planning without 
Uncertainty Margin (to be used 
for side-by-side comparison) 
Virtual evaluation of potential Delivery Error, 
Plan Optimization Error and Patient Outcome 

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Table of Contents 
SCHEMA ...................................................................................................................................... 3 
1.0 
BACKGROUND AND RATIONALE ............................................................................ 5 
1.1 
Proton Beam Therapy ............................................................................................... 5 
1.2 
Study Rationale ........................................................................................................... 5 
1.3 
CT scanning equipment ........................................................................................... 7 
1.4 
Investigational image reconstruction algorithms and radiological 
quantity modeling tools ....................................................................................................... 7 
2.0 
OBJECTIVES ................................................................................................................... 8 
2.1 
Primary Objectives ..................................................................................................... 8 
2.2 
Exploratory Objectives .............................................................................................. 8 
3.0 
PATIENT SELECTION ................................................................................................... 8 
3.1 
Inclusion of Women and Minorities ..................................................................... 8 
4.0 
REGISTRATION PROCEDURES ................................................................................ 8 
4.1 
Confirmation of Patient Eligibility ......................................................................... 8 
4.2 
Patient Registration in the Siteman Cancer Center OnCore Database .... 9 
4.3 
Assignment of UPN .................................................................................................... 9 
5.0 
STUDY PLAN .................................................................................................................... 9 
5.1 
Experimental procedures and data to be acquired ......................................... 9 
5.2 
Study endpoints and methods of analysis ....................................................... 10 
5.3 
Modeling of clinical impact .................................................................................... 11 
6.0 
SAFETY MONITORING AND REPORTING ........................................................... 12 
6.1 
Potential Risk ............................................................................................................. 12 
6.2 
Protection against Risk ........................................................................................... 12 
6.3 
Potential Benefits of the Proposed Research to the Subjects and Others
 
12 
6.4 
Adverse Events .......................................................................................................... 12 
6.5 
Data and Safety Monitoring Plan ........................................................................ 13 
7.0 
STATISTICAL CONSIDERATIONS ........................................................................... 13 
•  
 
 

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1.0 
BACKGROUND AND RATIONALE 
 
1.1 
Proton Beam Therapy 
 
Proton beam therapy (PT) is a rapidly evolving modality with high potential for improving 
radiation therapy clinical outcomes because of its ability to deliver high doses to the target 
tissue while sparing surrounding normal tissues. Because of the finite range of protons in 
tissue, proton dose rapidly peaks at a maximum value (call the “Bragg Peak”) near the end 
of the charged particle range and then abruptly falls to negligible levels.  By properly 
modulating the proton energy, and superposing many Bragg Peaks (the “spread-out Bragg 
peak” or SOBP), a single-field dose distribution is created which uniformly treats the tumor 
and falls to negligible levels within a few mm beyond the distal tumor margin, thereby 
sparing almost completely normal tissues located distal to the tumor.  However, PT dose 
delivery is very sensitive to the physical characteristics of the tissues in the beam’s path.  
Both the magnitude and position of the Bragg peak depends on the composition and density 
tissue of tissues traversed by the protons.  Recommended {ICRU, 1989 #1028; ICRP, 2003 
#904}  organ/tissue compositions and densities, available for radiotherapy planning, are 
based upon historic (mostly from 1930-1960) tissue sample measurements{White, 1988 
#1030; Woodard, 1986 #1031; Hammerstein, 1979 #1032} with large variations within the 
suggested values {Brooksby, 2006 #1052; Dance, 1999 #1033; Geise, 1996 #993}. These 
are potentially imprecise values that may vary significantly from patient-to -patient and 
within a single patient.   Due to the aforementioned uncertainties in tissue composition and 
density, it is customary to add a 3.5% (about 4 mm for 12 cm range protons) to ensure 
adequate tumor coverage in the face of these so-called proton range uncertainties.  
Unfortunately, these safety margins significantly reduce the potential for sparing normal 
tissue. 
 
Mitigating PT range uncertainties through more accurate characterization of tissue density 
and composition variability would support of more accurate prediction of dose delivered 
to the tumor target and adjacent normal tissues.  Current PT planning practices rely on 
standard single-energy CT imaging (SECT) which necessitates 3-6 mm range uncertainty 
margins{Moyers, 2001 #1843; Zhang, 2007 #1844} that significantly increases exposure 
of adjacent organs to high doses.  In theory, dual-energy CT imaging (DECT) can reduce 
the range uncertainty problem encountered within PT by non-invasively and more 
accurately measuring the radiological tissue properties needed to perform accurate PT dose 
calculations{Yang, 2010 #1853; Williamson, 2006 #923}.  However, as currently 
configured, commercial CT scanners cannot support the level of measurement accuracy 
required by DECT analysis {Williamson, 2006 #923} to significantly reduce range 
uncertainty margins .   
 
1.2 
Study Rationale 
 
The aim of this protocol is to refine the accuracy of PT by the use of DECT, in conjunction 
with novel iterative image reconstruction algorithms, to more precisely determine the tissue 
properties through which the proton beam path travels.  

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Plan quality in proton therapy is compromised by lack of accurate knowledge of proton 
stopping power, giving rise to dose prediction errors (DPEs) and optimization-convergence 
errors (OCEs). The DPEs are defined as the difference between the dose calculated by the 
treatment planning system and the actual dose delivered to the patient, mainly affected by 
the inaccurate prediction in the distal edge of a proton beam. OCE is defined as the 
difference between a clinically executed plan produced by an optimizer based on 
dosimetric approximations (e.g., SECT) and a better plan (e.g., based on DECT). 
Accounting for inaccuracies or uncertainty in proton stopping power, the planning 
optimizer must include a safety margin for the resulting range uncertainty and perform 
robust optimization using the distribution of errors. We hypothesize that both DPEs and 
OCEs arising from SECT SP mapping errors will give rise to measurable changes in 
clinical outcomes and that this loss of clinical efficacy can be reduced with more accurate 
estimation of proton stopping power using DECT.  
 
Knowledge gained from the DECT will be used to generate a mock PT plan for comparison 
purposes. Successful completion of this project is intended to measure potential increases 
in the accuracy with which cancers can be treated with proton therapy and to reduce the 
potential incidence of normal tissue toxicity. We will then evaluate whether the additional 
normal tissue dose sparing achieved in these mock plans could improve outcomes for 
patients undergoing proton therapy by reducing normal tissue complications or enabling 
tumor dose escalation in comparison to the more traditional 3-6 mm estimates of range 
uncertainty from Moyer et al. 
 
Uncertainty in proton range is incorporated into proton plan optimization in two ways: 
safety margin and robust optimization. Safety margin involves expanding CTV and OAR 
contours (generally by 3.5% of proton range) as recommended by ICRU 50 and 62.  Recent 
studies have demonstrated that robust optimization in proton therapy produces more 
consistent and predictable dose distribution through the whole treatment course.  Given the 
PDFs describing various uncertainties (setup, organ motion, and true vs. apparent SPR 
values), robust optimization selects multiple worst case scenarios (instances of patient 
anatomy and SPR assignments) from the ensemble of all possible instances described by 
the PDFs. The optimizer then optimizes the plan to satisfy the planning constraints and 
goals simultaneously on the multiple worst-case scenarios.  The resultant plan is robust in 
the sense that acceptable outcomes will be realized even in the face of the most unfavorable 
random outcomes (positioning and SPR errors) that are likely to occur. In clinical practice, 
volumetric imaging greatly reduces the uncertainty associated with setup and sheds light 
on pre-treatment reoptimization of the treatment plan. As the range uncertainty becomes 
the largest uncertainty in plan optimization, accurate information on proton stopping power 
helps the optimizer achieve a more optimal plan.  
 
Patient participation will be limited to having a DECT instead of the standard SECT.  That 
single DECT session will be used to generate two treatment plans; one using standard range 
uncertainty margins and the second using exact measures of tissue density from the DECT 
to generate a mock treatment plan for comparison.  We expect that the mock treatment plan 
will have tighter margins around the tumor and spare more normal tissue.  Eligible patients 

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will either have lung cancer or cancer of the brain or base of skull.  We have chosen these 
two patient populations because the tumor is adjacent to air densities where range 
uncertainty is greater, the problem poses the greatest technical challenge, and reducing the 
projected toxicity to critical structures would be clinically most meaningful (i.e. reducing 
dose to the esophagus or optic nerve). 
 
Application of novel CT imaging systems to human patients will allow their performance 
to be evaluated in a clinical context in ways that cannot be duplicated in phantoms or non-
human models.  By using human subjects, this research proposal is able to investigate the 
accuracy of dual-energy CT in the calculation of SPRs that significantly affect the outcome 
of patients treated with proton therapy. 
 
1.3 
CT scanning equipment 
 
State-of-the art commercially marketed CT simulators in the Department of Radiation 
Oncology will be used to acquire the research data needed for these projects.  Currently, 
the systems available to this project include the Philips Brilliance Big Bore CT/simulator 
(16 detector rows and 85 cm bore) and the Siemens Confidence CT/simulator (20 detector 
rows and 80 cm bore).  
In addition to acquiring medically-routine CT datasets, each patient subject will be scanned 
sequentially at two or three different beam energies (80 or 90 kVp, 100 kVp, 120 kVp, 
and/or 140 kVp) as part of this research study.  These additional scans will be acquired 
utilizing the FDA-approved workflow and scanner operating modes.  Sinogram data will 
be exported in raw, minimally processed form utilizing proprietary data export and 
processing tools provided to the investigators by the scanner vendors.  In no case, will the 
additional local imaging dose (in terms of CTDIvol) given to the patients by the two or three 
additional scans exceed 6 cGy per session. 
 
1.4 
Investigational image reconstruction algorithms and radiological quantity 
modeling tools 
 
The overall goals of the grant application (1R01CA212638, “Quantitative Dual-energy CT 
Imaging for Cancer Imaging and Radiation Therapy Applications”), is to develop, validate 
and implement in clinically useful forms novel iterative dual-energy x-ray CT image 
reconstruction algorithms which incorporate realistic models of CT data acquisition physics, 
including beam hardening, scatter, detector response, and measurement noise. Our promising 
preliminary results demonstrate tissue cross section maps that are more accurate and have lower 
uncertainty than either post-reconstruction dual-energy CT mapping processes or state-of-the-art 
single-energy stoichiometric tissue property mapping.  The connection between CT measurements 
and image intensity and PT-relevant radiological quantities is mediated by another innovation, the 
basis vector model (BVM) {Han, 2016}. 
 
Immediately following acquisition of sequential dual energy images from patient subjects, raw 
sinograms without beam-hardening or scatter corrections will be extracted with the vendor’s 
support, in addition to vendor reconstructed images and preprocessed sinograms.  The raw 
sinogram data will serve as input to the novel iterative DECT image reconstruction software. 
 

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2.0 
OBJECTIVES 
 
2.1 
Primary Objectives 
 
To evaluate 3D organ- and patient-specific distributions of various radiological quantities, 
including proton stopping powers.  
 
2.2 
Exploratory Objectives 
 
To assess the impact of DECT tissue property mapping on  
a.) plan quality  
b.) dose-delivery accuracy, and  
c.) simulated patient outcomes  
for locally advanced lung, H&N and brain tumor patients treated with proton therapy. 
 
 
3.0 
PATIENT SELECTION 
 
1. Diagnosis of histologically proven primary or metastatic cancer requiring thoracic radiation 
therapy OR radiation therapy to the H&N or brain. 
2. At least 18 years of age. 
3. Planning to undergo proton- or photon-beam radiation therapy as part of the clinical 
management of the diagnosed cancer. 
4. No implanted metallic objects excepting dental prostheses in the region to be scanned. 
5. No IV or oral contrast medium within 24 hours prior to DECT image acquisition. 
6. Not pregnant. 
7. Able to understand and willing to sign an IRB-approved written informed consent document. 
 
 
3.1 
Inclusion of Women and Minorities 
 
Both men and women and members of all races and ethnic groups are eligible for this trial.   
 
 
4.0 
REGISTRATION PROCEDURES 
 
Patients must not start any protocol intervention prior to registration through the Siteman 
Cancer Center. 
 
The following steps must be taken before registering patients to this study: 
 
1. Confirmation of patient eligibility 
2. Registration of patient in the Siteman Cancer Center database 
3. Assignment of unique patient number (UPN) 
 
4.1 
Confirmation of Patient Eligibility 

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Confirm patient eligibility by collecting the information listed below: 
 
1. Registering MD’s name 
2. Patient’s race, sex, and DOB 
3. Three letters (or two letters and a dash) for the patient’s initials 
4. Copy of signed consent form 
5. Completed eligibility checklist, signed and dated by a member of the study team 
6. Copy of appropriate source documentation confirming patient eligibility 
 
4.2 
Patient Registration in the Siteman Cancer Center OnCore Database 
 
All patients must be registered through the Siteman Cancer Center OnCore database. 
 
4.3 
Assignment of UPN 
 
Each patient will be identified with a unique patient number (UPN) for this study.  All data 
will be recorded with this identification number on the appropriate CRFs. 
 
 
5.0 
STUDY PLAN 
 
5.1 
Experimental procedures and data to be acquired 
Patient therapy will not be affected by enrollment in this study.  Patients enrolling in this study will undergo 
additional sequential DECT scans in addition to their routine SECT or DECT scans by enrolling in this 
study, which will increase imaging dose to the patients.  An additional two sequential scans of the thorax 
or H&N/brain will be acquired at scan settings ranging from 70kVp to 140 kVp (with or without additional 
filter) prior to initiating RT.  Typically, the experimental DECT imaging data will be acquired prior to the 
clinically-necessary CT simulation study in the same session.  Up to two additional experimental DECT 
imaging sessions should be scheduled when logistically feasible.  For example, if a patient subject requires 
re-simulation, DECT scan data should be acquired prior to the clinical simulation study in the same session.  
Raw sinograms without beam-hardening or scatter corrections will be exported from the scanner and 
archived with the vendor’s support.  In addition, clinical SECT and DECT images acquired as part of routine 
RT planning will be collected for this study.  For H&N and brain patients, the DECT scan volume should 
extend from the superior-most aspect of the cranium down to the suprasternal notch.  For the thoracic 
patients, the scan volume should encompass the entire lung parenchyma (apices to base).  
 
 
 

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Figure 1: Anatomy of a virtual clinical trial illustrating its two main components: a planning and delivery 
module that uses single energy CT reconstruction (SECT) for proton stopping power; and a module for 
estimating the dose actually delivered by the proposed dual energy CT (DECT) reconstruction for proton 
stopping power. In addition to comparing dose volume histogram (DVH) metrics as clinical outcome surrogates, 
outcome models such as generalized equivalent dose (gEUD) will be used. 
 
5.2 
Study endpoints and methods of analysis 
 
5.2.1 Definition of Virtual Clinical Trial 
 
Figure 1 illustrates the concept of virtual clinical trial (VCT), a relatively new method in 
radiotherapy to evaluate the impact of a new technology with greatly reduced cost and risk to 
participating institutes and patients. The trial takes full advantage of computer modeling and 
simulation to evaluate the benefits of proposed technology in patient outcomes.   In essence, the 
collected research images serve as an in silico model of the patient.  On each patient, the impact of 
various planning and image reconstruction strategies can be assessed over the patient population. 
 
5.2.2 Dose Evaluation Schema 
 
We will perform a VCT for each tumor site to assess quantitatively dose prediction errors (DPEs) 
and optimization-convergence errors (OCEs) associated with more approximate SECT stopping 
power mapping. In the VCT, every patient will go through the current clinical workflow for 
planning and dose delivery, where SECT is the primary image for treatment planning and 
conventional range uncertainty of 3.5% is employed. The SECT data uses one of the two DECT 
data sets. The DECT images will be used differently in Arm A and Arm B of the VCT. 
 
In Arm A, patients are treated with treatment plans optimized and calculated on the SECT data. 
Plan dose is re-calculated for every patient with the clinical plan in a Monte Carlo dose calculation 
engine for better accuracy. We will use TOPAS, an extension of Geant4 simulation toolkit, as the 
dose calculation engine.  
 

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In Arm B, the DECT data is used for plan optimization and dose calculation.  In Arm B1, DECT 
data is used to estimate the actual dose delivered using the clinical plan based on SECT data. In 
Arm B2, the plan is re-optimized on DECT data with the conventional uncertainty margin of 3.5% 
of proton range. In Arm B3, the plan is re-optimized on DECT data with the SPR uncertainties 
derived from the patient-specific uncertainty model developed. 
 
5.2.3 Definitions 
 
DPE. The fractional DPE is (𝐷𝐷𝐱𝐱
𝐷𝐷𝐷𝐷,𝐵𝐵1 −𝐷𝐷𝐱𝐱
𝑆𝑆𝑆𝑆,𝐴𝐴) 𝐷𝐷𝐱𝐱
𝐷𝐷𝐷𝐷,𝐵𝐵1
ൗ
, based on the difference in planned dose 
(Arm A) and delivered dose (Arm B1), where x is the dose-volume index and the superscript A and 
B1 denotes the dose plan from Arm A and B1 respectively. 
 
OCE. The fractional OCE is (𝐷𝐷𝐱𝐱
𝐷𝐷𝐷𝐷,𝐵𝐵2 −𝐷𝐷𝐱𝐱
𝐷𝐷𝐷𝐷,𝐵𝐵3) 𝐷𝐷𝐱𝐱
𝐷𝐷𝐷𝐷,𝐵𝐵2
ൗ
, based on the difference in planned dose 
optimized on DECT with the conventional uncertainty margin (Arm B2) and the SPR uncertainties 
derived from the patient-specific uncertainty model developed in Arm B3. 
 
Combined. The total change in dose between optimized plans from SECT and DECT data. 
 
5.3 
Modeling of clinical impact 
 
In order to quantify the reduction of DPEs and OCEs in clinically relevant terms and to resolve 
ambiguities associated with purely dosimetric outcome indices, radiobiological isoeffect models 
and outcome prediction models will be used. To account for local delivery variations (dose rate, 
fraction size, linear energy transfer) in relation to local radiosensitivity, we will use the isoeffective 
quantity, biologically effective dose or BED, derived from the linear-quadratic (LQ) model.  LQ 
parameters from photon therapy clinical data will be adapted to account for the fact that linear 
energy transfer varies along the proton path, producing variations in relative biological 
effectiveness (RBE) which is maximized near each pristine Bragg peak. As higher linear energy 
transfer (LET) produces more single-and double-strand breaks, tissues near the distal SOBP act 
more like early responding tissues with higher α/β compared to photon treatments.  Assuming a 
first-order approximation for the dependence of the proton αp parameter on LET (evaluated by 
TOPAS), it can be approximated by αp=αx+χαx/βx•LET, where αx and βx are photon therapy 
LQ parameters. We found χ to be 0.0079 µm keV-1 Gy-2 from fitting the experiment data20 to 
proton therapy. BED, rather than physical absorbed dose, will be used as a dose accumulation 
parameter, giving rise to BED volume-histograms. 
 
Outcome models take this process one step further by transforming a complex 3D dose distribution 
into a single number that is a surrogate for a clinically measurable endpoint.  Equivalent uniform 
dose (EUD) was introduced by Niemierko in 1997 as the primary surrogate for tumor control 
probability (TCP). For quantifying normal tissue responses, we use Mohan’s approach, which 
reduces the cumulative BED volume histogram to a single equivalent dose using generalized EUD 
(gEUD) and applies the LKB model to compute the normal tissue complication probability 
(NTCP). 
 
We will quantify difference in tumor control and normal tissue complications between VCT trial 
arms A and B for each relevant tumor and normal-tissue endpoints. For brain, the complication 
endpoints to be reviewed include brainstem injury, panophthalmitis/blindness and brain necrosis; 
for lung, the complication endpoints to be reviewed include esophagitis, pneumonitis, and cardiac 
morbidity.  
 

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6.0 
SAFETY MONITORING AND REPORTING 
 
6.1 
Potential Risk 
 
In addition to potential loss of privacy, the delivery of small additional doses of ionizing radiation 
associated the additional DECT research scans poses a small risk of carcinogenesis to patient 
subjects.  The total additional radiation dose for the dual-energy data acquisition will be less than 
6 cGy per imaging session, or a total of no more than 24 cGy over the entire treatment course.   The 
potential risk of cancer death due to this modest additional dose of ionizing radiation is theoretical 
and acknowledged to be uncertain.  It is estimated that there is a 0.0002 incidence of increased 
cancers, over a risk period of 30 years, per 1 cGy radiation.   No contrast media or other invasive 
procedures will be required. 
 
6.2 
Protection against Risk 
 
The potential risk of increased radiation exposure due to increased imaging is currently only known 
from theoretical analyses.  It is estimated that there is a 0.0002 incidence of increased cancers, over 
a risk period of 30 years, per 1 cGy radiation.  This small radiation dose will be overwhelmed by 
therapeutic doses (30-90 Gy) administered to the region being imaged.  There are no study-related 
limitations on patient care.  No subjects will be enrolled prior to IRB approval.  The imaging 
systems to be used are all FDA approved devices. The risk to patient privacy will be mitigated by 
anonymizing the images and raw datasets, so that patient identity is known only to the protocol PI 
and data managers, prior to processing the data.  
 
6.3 
Potential Benefits of the Proposed Research to the Subjects and Others 
 
The protocols of this project will not alter the participating subject’s therapy beyond the limits 
specified in the eligibility requirements and protocol conduct.  In particular, these protocols place 
no limits on the use of the acquired imaging data to manage the patient’s radiation therapy.  The 
treating radiation oncologists may use this imaging data and its results to improve the participating 
subject’s therapy, provided that all applicable FDA and IRB guidelines are followed.  Beyond this 
possibility, no direct benefit to the patient is expected.  The alternative is not to participate in the 
study.  
 
The overall hypothesis of this study is that dual-energy CT imaging, when coupled with appropriate 
image reconstruction and analysis techniques, can be used to accurately map the radiological 
properties (total, scattering, and photoelectric cross sections; stopping power ratios and multiple-
scattering cross sections) of human tissue on a voxel-by-voxel basis.  Moreover the data will 
provide preliminary data to design a broader trial to assess the hypothesis that cross-section imaging 
significantly improves dose delivery accuracy.  This will increase the accuracy with which cancers 
can be treated with charged particle radiation therapy and reduce the incidence of normal tissue 
toxicity. The additional normal-tissue dose sparing may improve outcomes to future patients 
undergoing treatment by reducing normal tissue complications or enabling tumor dose escalation.  
 
6.4 
Adverse Events 
 
There are no anticipated study-related adverse events, aside from those listed in the Potential Risks 
section (above).  All unanticipated adverse events will be reported to the IRB, as required within 
10 days of notification.  All serious unanticipated adverse events will be reported within 24 hours. 
 

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The descriptions and grading scales found in the revised NCI Common Terminology Criteria for 
Adverse Events (CTCAE) version 4.0 will be utilized for adverse event reporting. 
 
6.5 
Data and Safety Monitoring Plan 
 
The study principal investigator and study coordinator will monitor for breaches of confidentiality 
and other adverse events on an ongoing basis.  Once the PI or study coordinator becomes aware of 
a reportable adverse event, the event will be reported to HRPO and QASMC according to 
institutional guidelines.  This study does not require QASMC audit or submission of DSM reports. 
 
 
7.0 
STATISTICAL CONSIDERATIONS 
 
Based on the differences and patient-to-patient variability from our preliminary data, recruiting 20-25 
patients for each site (depending on DVH endpoint) is sufficient to detect 2%-3% changes in absolute DVH 
surrogates, e.g., 2% changes in heart V30 for 20 NSCLC patients, with 80% power at 95% level of 
confidence from reducing range uncertainty from 3.5% to 1.0%.  Because each patient subject is their own 
control, we assumed a moderate positive correlation between treatment arms of 0.5, consistent with that 
observed in other image-guided radiation therapy VCTs40. For lung cancer, we hypothesize that we will 
observe >40% reductions in esophageal V60 (corresponding to reduction of ≥g2 esophagitis from 60% to 
39% (71), a reduction >30% in mean heart dose (from 8 Gy70 to 5 Gy, reducing relative risk factor for late 
cardiac disease from 1.60 to 1.3727, and total lung V20 from 15% to 10% (lowering risk of radiation 
pneumonitis from 15% to 8%48). Likewise, greater precision towards predicting radiation dose to 
neighboring neural structures such as the optic chasm, optic nerve, brainstem, and eye is clearly important. 
Tumor volumes frequently abut these structures, creating situations where the physician chooses to 
underdose the tumor target, rather than risk severe toxicity in normal tissue resulting in complications such 
as blindness. 
 
The hypothesis to be tested is that Monte Carlo treatment plans based upon standard of practice single-
energy CT cross-section mapping differ significantly from Monte Carlo plans using cross-section maps 
derived from our proposed quantitative dual-energy CT (DECT) imaging methodology.   Neither the 
expected magnitude of the difference nor its variability is known.   However, from the literature and our 
preliminary studies, a minimum of 10%-15% difference might be considered clinically significant and the 
standard deviation of the difference is expected to be about 15%.     Using a paired t-test, a sample size 
n=20 per group would enable us to detect differences of 15% and 12% for percent standard deviations of 
15% and 10%, respectively, with 80% power at the 5% significance level.  If we relax the significance 
requirement to 10% (appropriate for a preliminary/exploratory study), differences of 13% and 8.5% will be 
detectable under these conditions.