Uncertainties in image‐based assessment of Radiation Therapy
James Balter
University of Michigan
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Disclaimers
• Advisor, Calypso Medical Technologies
• Supported by NIH P01CA59827
Confession
• I may have nothing new (or useful) to say
Depression
• I will still spend ~40 minutes searching for something useful to tell you!
Objective
• Therapy assessment is a critical element of:
– Individual patient treatment outcome evaluation
– Clinical trial design
– Population‐based retrospective evaluation of treatment results
• It is also a potential driving force in individualizing treatment mid‐course
Objective
• The imaging methods used, display and analysis techniques employed, standards (or lack thereof), and fundamental human variations all contribute to uncertainties in image‐based assessment
• This talk will explore some image‐based assessment methods, explore a few evolving paradigms and techniques, and needs for quality control and validation of imaging for therapy assessment
Longitudinal assessment
Image
Treat
Image
Tumor size
• Some solid tumors visibly change size as therapy progresses
• Size change is often correlated with response New Drug Kills Cancer with Few Side Effects
The incredible shrinking tumor These CT scan images–
showing the cross section of the patient’s abdomen–depict how her ovarian tumor (denoted by the red circle) shrunk in response to a new anticancer drug called olaparib. The patient carries a mutation in the BRCA1 gene, impairing her tumor cells’ ability to repair DNA damage. Olaparib delivers another blow to the cancer cells’ DNA repair machinery, killing them outright. Credit: The New England Journal of Medicine
Response Evaluation Criteria in Solid Tumors
(RECIST)
• RECIST (Response Evaluation Criteria In Solid Tumors) is a set of published rules that define when cancer patients improve ("respond"), stay the same ("stable") or worsen ("progression") during treatments.
• The original criteria were published in February 2000 by an international collaboration including the European Organization for Research and Treatment of Cancer (EORTC), National Cancer Institute (NCI) of the United States and the National Cancer Institute of Canada Clinical Trials Group.
• RECIST 1.1, published in January 2009, is an update to the original criteria. Today, the majority of clinical trials evaluating cancer treatments for objective response in solid tumors are using RECIST.
• Source: www.recist.com
RECIST 1.1 attempts to enforce consistency
• Cancer trials are increasingly complex, involving dozens or even hundreds of investigators from centers around the world. While the RECIST rules are highly dependent upon measurement of tumor size, different clinicians may vary greatly in their methods of performing these measurements.
• Consistency in following the imaging requirements and rules is even more challenging. When investigators vary in how they follow RECIST as a trial endpoint, the study results may be placed in jeopardy by significant levels of variability.
Source: recist.com
Some key elements of RECIST
Target lesion selection
Measurement rules
Consistency of repeat scans
Guidelines for use of PET, and non‐axial images (e.g. MRI)
• Definitions of:
•
•
•
•
– PD (progressive disease) – 20% increase
– CR (complete response) – disappearance of target lesion(s)
– PR (partial response) – 30% decrease
Cone beam CT images of a patient with oropharyngeal
cancer at the first (left) and 35th (right) fraction of treatment. RECIST 1.1 specifies measurement of the largest single dimension of the tumor as an indication of response.
RECIST – Need for consistent imaging modality and acquisition protocols
Source: recist.com
“RECIST is simple. Using RECIST is not.”
Source: recist.com
CT imaging – the gold standard?
• Radiation Oncology places significant faith on the accuracy of CT imaging for:
– Patient Geometry
– Electron Density assignment
• How accurate is modern CT?
Current and Future Clinical
Applications of Quantitative CT
John M. Boone
Departments of Radiology and Biomedical Engineering
University of California, Davis
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John M. Boone
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Scatter to primary ratio (SPR) = 
John M. Boone
Water reference SPR = 5%, measurement SPR different
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2%
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4%
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John M. Boone
Water reference SPR = measurement SPR
soft tissue
John M. Boone
1%
2%
3%
4%
5%
Different vendors have different HU values
John M. Boone
CT still gives accurate volume definition, right?
• More on this later….
Molecular Imaging
• (FDG) PET is commonly used to visualize tumor extent (metabolic surrogate)
• FDG and other agents have been considered as tools to aid in “dose painting”
– Metabolism surrogate: FDG
– Hypoxia surrogates: FDG, FAZA, F‐Miso, Cu‐ATSM
– Proliferation surrogate: FLT
– Liver Metabolism (HIDA)
–…
I am not (yet) a dose artist…
FLT (proliferation) image
Courtesy of R Jeraj, UW)
Balter BWH 2010
70 Gy
60 Gy
50 Gy
FDG PET changes in RT of NSCLC
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FDG PET
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M e ta b o lic n o n -re s p o n d e rs
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max
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A v e ra g e
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T im e [d a y s ]
Baardwijk et al 2007, Radiother Oncol, 82: 145.
Courtesy of Robert Jeraj, UW
Sensitivity and specificity issues for metabolic imaging
• Consistency of images
– Scanner calibration
– Injected activity
– Scan time
– Reconstruction options
• Patient variations
– Baseline metabolism
– Non‐related tissue uptake (e.g. inflammation)
R Boellard, JNM 50 (5 Suppl), 2009
• Review of factors influencing quantitative PET
• Included technical, biological and physical factors
Peter Scully et al (Wisconsin):
Optimizing PET imaging protocol parameters
Reconstruction Parameters
Recon
Acquisition
Grid Size
Algorithm
Iterations
Post filter
A
2D
128 x 128
2D OSEM
2
3 mm
B
2D
128 x 128
2D OSEM
2
5 mm
C
2D
128 x 128
2D OSEM
4
5 mm
D
2D
256 x 256
2D OSEM
2
3 mm
E
2D
256 x 256
2D OSEM
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3D
128 x 128
3D OSEM
2
3 mm
G
3D
128 x 128
3D OSEM
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H
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128 x 128
3D OSEM
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3D OSEM
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J
3D
256 x 256
3D OSEM
2
6 mm
Scully et al – optimize parameters based on recovery of volumes of known object size and activity
Scully et al
Recovery Coefficient
1.2
1.0
0.8
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B
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D
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Reconstruction
I
J
PET signal versus tumor response for chemotherapy. A tumor that is not responding sufficiently to
treatment after 2 cycles of chemotherapy (blue dashed line) may be predictable from PET changes,
whereas one that would respond after 4 cycles (red) may be indistinguishable from one that may
require more aggressive treatment (black) as signals below the PET detection threshold still may
involve several logs of viable cells. (adapted from Kasamon YL, Jones RJ, Wahl RL. J Nucl Med. 48
Suppl 1:19S-27S, 2007) – R. Wahl, JHU
Physiological imaging
• Imaging as a surrogate for physiological endpoints
– Ventilation: SPECT, polarized gas MRI, 4D CT
– Perfusion: MAA SPECT, Dynamic contrast enhancement (MRI, CT)
– Diffusion (as a surrogate for tumor cellularity, integrity of nerves): Diffusion weighted imaging
–…
Mayr et al IJROBP 77(2), 502‐8, 2010
Perfusion
• Many different methods of acquiring and analyzing data
• Multiple metrics: total perfusion, “area under curve”, initial slope, mean transit time, transfer coefficients, local blood volume,…
Dynamic Contrast Enhanced (DCE) imaging for perfusion analysis
• Inject contrast
• Acquire a cine series of image volumes
• Analyze within regions or voxels:
– Reference input (arterial, venous) signals
– Test tissue signals
• Derive parameters from model fit to these signals
Mayr et al IJROBP 77(2), 502‐8, 2010
Examples of perfusion related models
• Brain perfusion model (Ostergaard MRM 1999)
– Brain perfusion
•
Kety model (Toft JMRI 1999)
– Transfer constant to tissue and blood volume
• Dual‐input and single compartment liver perfusion model
– Arterial and portal venous perfusion Cao AAPM2009
t
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Choose acquisition sequence, parameters and models
• What is targeted in the • choose right acquisition, clinical trial
parameters and model
– Anti‐VEGF therapy
in recurrent GBM
– Ktrans and blood volume
• Toft (Kety) and CBV models
Sorensen et al, Cancer Research 2009
Cao AAPM2009
Choose acquisition sequence, parameters and models
• What is targeted in the • choose right acquisition, clinical trial
parameters and model
– Chemo + RT
In HN Cancer
Local control
– Blood volume and flow
• Toft and blood flow models
Local failure
17mL/100g
Pre RT
Cao AAPM2009
BV after 2 weeks RT
Cao, et al Red Journal 2008
Choose acquisition sequence, parameters and models
• What is targeted in the • choose right acquisition, clinical trial
parameters and model
– Radiation effect on liver – Portal venous perfusion
function
• Dual‐input, single compartment liver perfusion model
Cao AAPM2009
Motion needs to be considered
Before registration
Cao AAPM2009
After registration
Perfusion CT involves potentially significant dose (even by RT standards)
30 – 50 CT scans at the same location
perfusion
A. perfusion imaging John M. Boone
time to peak enhancement
blood volume
B. computed parametric images
Safety of new imaging protocols
Some organized efforts for standardizing quantitative imaging
• Quantitative Imaging Network
• Quantitative Imaging Biomarker Alliance (QIBA)
The Quantitative Imaging Network (QIN)
• PAR‐11‐150; a U01 mechanism issued originally in August 2008, and reissued in March 2011.
– No set‐aside funds and no limit to the number of awards
• Submission dates:
– February 5, June 5, and October 5
– Electronic submissions through grants.gov
• Reviewed by Special Emphasis Panel (SEP) from NCI
The QIN Program
• Our hypothesis: Optimized and validated quantitative imaging techniques are needed on commercial platforms for large scale multi‐center clinical trials focused on therapy response.
• Therefore: Necessary to promote innovative research in this area.
• Goal: Bring quantitative imaging as a validated method or tool to oncologists to support clinical decision making.
R Nordstrom, NIH
Current QIN Status
• Currently, 9 network members
– Two more teams in position to be funded
– Several face‐to‐face meetings
• Broad range of imaging modalities
– CT and PET/CT
– SPECT
– MRI, DCE‐MRI, DW‐MRI
• Phantom studies, quality assurance
• Longitudinal studies
• Database development and sharing
R Nordstrom, NIH
The Present Members of QIN
Univ. of Washington
Brigham & Women’s Hospital
Columbia Univ.
Mass. General Hospital
Stanford Univ.
Univ. of Iowa
Univ. of Pittsburgh
Vanderbilt Univ.
H. Lee Moffitt
R Nordstrom, NIH
QIN working groups
• Data Collection (Barbara Croft)
• Image Analysis and Performance Metrics (Huiming Zhang and Keyvan Farahani)
• Bioinformatics/IT and Data Sharing (Larry Clarke)
• Outreach: External/Industrial Relations (Pushpa Tandon)
• Clinical Trial Design and Development (Barbara Galen)
QIBA
• Mission: Improve the value and practicality of quantitative biomarkers by reducing variability across devices, patients, and time
• Current committees:
– DCE MRI
– fMRI
– FDG‐PET
– Volumetric CT
– COPD‐Asthma
How do we know that everything works?
• Accuracy – How close you are to the truth
• Precision – How reproducible your measurement is
In vivo evaluation – test retest
• Perform imaging through analysis on the same subject without therapeutic intervention
• Reasonably incorporates multiple sources of uncertainty
Frings et al JNM 51(12), 2010
Phantom development
• The need for quantitative imaging phantoms is well established
• The design and propagation of useful phantoms is not
Volume characterization phantom (CT‐QIBA)
(b)
(a)
• The FDA has a research lab (N Petrick) developing image volume assessment phantoms
N Petrick, FDA
Spherical
Ellipsoid
Lobulated
Spiculated
(c)
FDA ‐ Phantom images
Current: 2 fixed densities (‐630, +100 HU)
N Petrick, FDA
Future: mixed density
N Petrick, FDA
One example analysis – slice thickness effect on ellipse volume measurement
N Petrick, FDA
Perfusion phantom 1 – QIBA (Jackson et al)
• Designed for support of DCE MRI studies
• Inserts of known T1 in a static phantom
Perfusion phantom 2 – PMH (Driscoll et al)
A) Perfusion Pump
(A)
B) Injection Pump
(B)
C) Flow Phantom
(D)
D) Flow control valves
(E)
(C)
Driscoll, Coolens, Keller et al.
E) Combined Flow
PMH phantom
Tested the range of the phantom with two different input flow rates and different contrast injection boluses
Driscoll, Coolens, Keller et al.
Perfusion Phantom 3 –UM (Thomas et al)
Vascular branching network
Compartments to yield differential sub‐voxel perdusion
Synthetic data
• Good for algorithm and software testing/validation
• Removes imaging experiment bias
• QIBA has synthetic DCE data as a project under the DCE‐MRI group (Barboriak – Duke) ‐
https://dblab.duhs.duke.edu/wysiwyg/downl
oads/README_dcemri_testdata.txt
Synthetic Data – Cao (UM)
•
Simulated DCE phantom for testing the standard DCE model (general Toft
model)
Parameters considered: CNR, temporal resolution, dynamic acquisition time, Ktrans, •
•
Vp, kep, input function temporal jitter, …
>1 million simulations to cover a large range of variations of parameters
2500 simulations for each combination of the parameters
•
12
Relative SD% of Vp
K=0.01, vp=0.01,kep=0.07
K=0.05, vp=0.2, kep=0.3
K=0.1 , vp=0.05, kep=0.7
K=0.5, vp=0.1, kep=3.3
K=0.001,vp=0.01,kep=0.007
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Relative SD% of K
trans
20
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5
K=0.01, vp=0.01,kep=0.07
K=0.05, vp=0.2, kep=0.3
K=0.1 , vp=0.05, kep=0.7
K=0.5, vp=0.1, kep=3.3
K=0.001, vp=0.01, kep=0.007
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Cao AAPM2009
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e.g. Ktrans and Vp do not have same sensitivity to noise!
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What do we need?
• Data acquisition standards
– Imaging systems and acquisition/reconstruction methods that are designed to be reproducible
• Design of systems
• Standard use protocols
• Incorporation of acquisition (and analysis) parameters within the framework of the image data
What do we need?
• Methods and procedures for maintaining and evaluating quantitative imaging systems
– Phantoms
– standard data for testing analysis methods
– common algorithms/language of analysis
What do we need?
• Education
– Most current quantitative imaging issues do not yet impact actual treatment planning in typical clinics yet
– their emergence (as well as our growing dependence on PET, automated image segmentation, and deformable registration) hints at educational and infrastructure needs to support practice changes in our near future
Summary
• Image‐based therapy assessment is heavily used in clinical trials, and is emerging in routine radiotherapy
• Imaging systems and their use need careful characterization, validation, and QA to support quantitative use
• As Radiation Oncology departments are routinely using advanced imaging, image‐based quantitative evaluation is emerging as a growing educational and support need for medical physicists
RECIST 1.1 – consistent contrast use