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."

1. 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

2. 2
 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

3. 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. 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. 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

6. 6
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

7. 7
Magnetic resonance imaging (MRI)

8. 8
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

9. 9
• Increasing cell density (cellularity); more cell membranes
per unit distance to hinder diffusion  lower ADC
ADC
• ADC depends on cell volume fraction
• Tumor cellularity may be monitored by DWI
Hall et al. Clin Canc Res 2004;10:7852Anderson et al. Magn Reson Imaging. 2000;18:689-95.
Diffusion
weighted
MRI

10. 10Arlinghaus 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)

11. 11
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.

12. 12
Sensitivity
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.

13. 13
Positron emission tomography (PET)

14. 14
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

15. 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

16. 16
• 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?

17. 17
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

18. 18
• 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

19. 19
• 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

20. 20
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

21. 21
• 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.”

22. 22
• 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

23. 23
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. 24Preziosi. 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. 25Atuegwu et al. Transl Oncol. 2013;6:256-64
Experimental Simulated
r = 0.90
Experimental
Simulated
Pearson’s correlation = 0.83 (p = 0.01)
Concordance correlation = 0.80
Nsimulated(t3) x106
Nexperimental(t3)x106
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%

26. 26
• 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

27. 27
• 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

28. 28
• 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

29. • 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

30. 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.

31. Predicted tumor
cell number
Observed tumor
cell number
Hormuth et al. 2013 Oak Ridge National Lab BSEC conference.

32. 32
• 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

33. 33
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.