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This is a talk from the Technology Association of Louisville Kentucky. Dawn Yankeelov is co-chair of TALK, and Dr. Thomas Yankeelov is the director for the Institute of Imaging Science at Vanderbilt University. He presented his latest research in June 2013, "Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth."
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Tom Yankeelov, Ph.D.
Ingram Associate Professor of Cancer Research Institute of Imaging Science
Departments of Radiology, Biomedical Engineering, Physics, and Cancer Biology
Vanderbilt University
19 June 2013
Integrating Advanced Imaging and Biophysical Models to Predict Tumor Growth
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Weather models use observations of the atmosphere to predict how wind, temperature, and humidity evolve in time
DW-MRI Cell Number
DCE-MRI Perfusion
FDG PET Metabolism
Noninvasive, quantitative imaging enables the same approach for predicting how tumors evolve in time
Yankeelov et al. Sci Transl Med. 2013;5:187ps9
Visit 2 25 15 40
+5%
Visit 3 28 16 44
+16%
Visit 4 32 18 50
+32%
Visit 5 48 23 71
+89%
Baseline 24 14 38
T1: T2:
SLD: (% )
Courtesy of Rick Abramson, M.D.
8 weeks 16 weeks
24 weeks
T2 T1
3
4
Working hypothesis:
Readily-available, multi-scale imaging techniques can provide the data to initialize/constrain predictive models of tumor
growth and treatment response for clinical application.
5
Outline
1. What can quantitative imaging offer oncology?
2. Specialize to neoadjuvant therapy
3. Lessons from weather forecasting
4. Towards a science of tumor forecasting
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Outline
1. What can quantitative imaging offer oncology?
2. Specialize to neoadjuvant therapy
3. Lessons from weather forecasting
4. Towards a science of tumor forecasting
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Magnetic resonance imaging (MRI)
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Diffusion weighted MRI
• Boundaries may reduce distance molecules travel when compared to free molecules
• Thus, the Apparent Diffusion Coefficient (ADC) is lowered
~√t
Distance from
original position
Free
Restricted
• Water molecules wander about randomly in tissue (Brownian Motion)
• In a free solution, after a time t, molecules travel (on average) a distance L from where they started
• But in tissue, compartment effects may hinder movement = restricted diffusion
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• Increasing cell density (cellularity); more cell membranes per unit distance to hinder diffusion lower ADC
AD
C
• ADC depends on cell volume fraction
• Tumor cellularity may be monitored by DWI Hall et al. Clin Canc Res 2004;10:7852 Anderson et al. Magn Reson Imaging. 2000;18:689-95.
Diffusion weighted MRI
10 Arlinghaus et al.
Diffusion weighted MRI, clinical example
Responder ΔADC = 11.8%
Non-responder ΔADC = -11.6%
Pre-NAC Post-1 cycle
ROC Analysis Sensitivity = 0.64 Specificity = 0.93
AUC = 0.70
Sensitivity = true positive rate = TP/(TP+FN)
Specificity = true negative rate = TN/(FP + TN)
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Dynamic contrast enhanced MRI (DCE-‐MRI)
• Each voxel yields a signal intensity time course that can be analyzed with a mathematical model
• By fitting data to model, extract parameters that report tissue characteristics
• Serial acquisition of images before, after an injection of contrast agent (CA)
• As CA perfuses into tissue, changes the measured signal intensity
plasma
space
tissue space Ktrans
Ktrans/ve
Cp(t)
Ct(t)
Ktrans = transfer rate constant
ve = extravascular extracellular volume fraction
Tofts, et al. J Magn Reson Imaging 1999;10:223-‐232.
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Sens
itivi
ty
ROC Analysis Sensitivity = 0.81 Specificity = 0.75
AUC = 0.80
When combining DW-MRI & DCE-MRI data: Sensitivity = 0.88 Specificity = 0.82
AUC = 0.86
Pre-therapy Post-1 cycle Post therapy
Responder
Non-Responder
DCE-‐MRI, Clinical Example
Li et al. Magn Reson Med 2013 (epub ahead of print; Mani et al. J Am Med Inform Assoc. 2013;20:688-95.
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Positron emission tomography (PET)
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18FDG
(blood)
18FDG
(tissue)
18FDG-6-PO4
(cells) X
Pre-NAC Post-1 cycle Post-all NAC
Non-responder
pCR
Li et al. Eur J Nuc Med &Mol Imaging Res 2012;2:62
Pre-NAC
extravascular extracellular volume fraction
plasma volume fraction
cellularity
FDG-PET (glucose metabolism)
blood perfusion and permeability
Post-1 cycle
Post-all NAC
Xia Li, Nkiruka Atuegwu, Lori Arlinghaus
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• Dramatic increases in quality of data available from non-invasive imaging
Imaging is moving from qualitative anatomical measurements to quantitative functional measurements at physiological, cellular, & molecular levels
• We talked about:
MRI—anatomy, blood vessels, blood flow, cellularity
PET—metabolism
Imaging Summary
• Other imaging measurements we did not talk about:
cell proliferation, pH, pO2 (MRI)
Receptor expression, apoptosis (PET & SPECT)
What is the best way to use such data to assist oncology?
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Outline
1. What can quantitative imaging offer oncology?
2. Specialize to neoadjuvant therapy
3. Lessons from weather forecasting
4. Towards a science of tumor forecasting
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• Pre-operatively treated locally advanced cancers chemo-, radiation, endocrine, and targeted therapies
• Five theoretical advantages to NAT:
1) Earlier treatment of occult metastatic despite “curative” resection
2) Determine sensitivity to treatment while tumor is in situ
3) Complete a full course of treatment (more likely pre-operatively than post-operatively)
4) Identify patients who develop metastases on NAT as they are unlikely to benefit from resection
5) Decrease primary tumor volume
Sener. JSO 2010;101:282
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• Key point: several disease sites where if you can get the patient to respond in the neoadjuvant setting improved outcome
• How to identify non-responders from responders early in therapy?
Develop predictive models that use data obtained from individuals
• But more than that—would enable patient specific “clinical trialing”
Can perform individualized, in silico clinical trials to optimize the drug regiment, order, timing, dose, etc.
Models must make specific predictions
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Outline
1. What can quantitative imaging offer oncology?
2. Specialize to neoadjuvant therapy
3. Lessons from weather forecasting
4. Towards a science of tumor forecasting
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• In order to have meaningful weather predictions, needed: 1) Rudimentary understanding of atmospheric dynamics 2) Regular radiosonde measurements (...and then satellite data) 3) Stable numerical methods 4) Electronic computers
• 100 years ago, none of this existed
Lynch. J Computational Physics 2008;227:3431-44.
“A century ago, weather forecasting was a haphazard process, very imprecise and unreliable. Observations were sparse and irregular, especially for the upper air and over the oceans. The principals of theoretical physics played little or no role in practical forecasting: the forecaster used crude techniques of extrapolation, knowledge of local climatology and guesswork on intuition; forecasting was more an art than a science.”
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• So how did meteorology make such dramatic advances? Advances in atmospheric physics, numerical methods, computing
Better data!
Lynch. J Computational Physics 2008;227:3431-44.
National Climate Data Center NEXRAD (NEX generation RADar) Data Sites
~160 sites
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Outline
1. What can quantitative imaging offer oncology?
2. Specialize to neoadjuvant therapy
3. Lessons from weather forecasting
4. Towards a science of tumor forecasting
24 Preziosi. Cancer Modeling and Simulation.
• Let the tumor cells proliferate up to a certain “carrying capacity” = "
• Solution is given by:
• The last equation states that the tumor cells will continue to grow (exponentially) up to the carrying capacity of the system determined by "
25 Atuegwu et al. Transl Oncol. 2013;6:256-64
Experimental Simulated r = 0.90
Experimental
Sim
ulat
ed
Pearson’s correlation = 0.83 (p = 0.01) Concordance correlation = 0.80
Nsimulated(t3) x106
Nex
peri
men
tal (
t 3) x
106
Some summary stats (n = 27):
• k (proliferation rate) separated pCR and non- pCR after 1 cycle of NAC (p = 0.019)
sensitivity = 82%, specificity 73%
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• Going forward, need to make greater use of available data
• An example mathematical model :
Random dispersal of tumor cells (diffusion)
Proliferation of tumor cells
Rate of chance of # of
tumor cells
ADC values from DW-MRI to assign NTC(r,t) and extract k(r)
Everything on the right hand side is known
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• Going forward, need to make greater use of quantitative, multi-modality data
• An example mathematical model :
Random dispersal of tumor cells (diffusion)
Proliferation of tumor cells
Rate of chance of # of
tumor cells
Rate of change of # of
endothelial cells
Diffusion of EC Chemotaxis of EC
Assume chemotaxis is in direction of areas of proliferating cells of higher density; can
also estimate this from DW-MRI data
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• Going forward, need to make greater use of quantitative, multi-modality data
• An example mathematical model :
Rate of change of # of
endothelial cells
Diffusion of EC Chemotaxis of EC
Rate of change of O2
Random dispersal of tumor cells (diffusion)
Proliferation of tumor cells
Rate of chance of # of
tumor cells
Perfusion data from DCE-‐MRI
ADC & PET data
• Going forward, need to make greater use of quantitative, multi-modality data
• An example mathematical model :
Rate of change of O2
Rate of change of glucose
Rate of change of # of
endothelial cells
Diffusion of EC Chemotaxis of EC
Random dispersal of tumor cells (diffusion)
Proliferation of tumor cells
Rate of chance of # of
tumor cells
Experimental system -- C6 glioma
Anatomical (Registration)
DW-MRI Cell Number
DCE-MRI Ktrans,ve, and vp
18F-FDG PET
MRI on days 9, 10, 11, 13, 15, and 17 PET on days 9, 15, and 17
Hormuth et al. 2013 Oak Ridge National Lab BSEC conference.
Predicted tumor cell number
Observed tumor cell number
Hormuth et al. 2013 Oak Ridge National Lab BSEC conference.
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• Having a model, driven by patient specific data would enable personalized, in silico therapy modeling theoretical/predictive oncology
• Could “give” the patient therapy in silico, then see how they “respond”
Could systematically adjust therapies, order of combination therapy, dosing scheduling, etc.
Since the quantitative imaging data can be acquired in 3D, at multiple time points and noninvasively, it is the only game in town
Could enable (more) rational clinical trials design/execution
Eminently testable in pre-‐clinical setting… and is translatable
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Squad • Lori Arlinghaus, PhD • Nkiruka Atuegwu, PhD • Richard Baheza, MS • Stephanie Barnes, PhD • Jacob Fluckiger, PhD • David Hormuth, BS • Xia Li, PhD • Mary Loveless, PhD • David Smith, PhD • Jared Weis, PhD • Jennifer Whisenant, MS • Jason Williams, PhD
Collaborators • Vandana Abramson, MD • Bapsi Chak, MD • Ingrid Mayer, MD • Mark Kelley, MD • Brian Welch, PhD • Rick Abramson, MD • Mike Miga, PhD • Vito Quaranta, MD
Funding • NCI U01 CA142565 • NCI R01 CA138599 • NCI R01 CA129661
• NCI R25 CA092043 • NCI R25 CA136440 • NCI P30 CA68485 & VICC
Very sincere thank you to the women who participate in our studies.
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