Acknowledgement
Development of
Therapy Response Models
Based upon Functional MRI
NIH/NCI 3 P01 CA59827
NIH/NINDS/NCI RO1 NS064973
NIH/NCI R21 CA113699
NIH/NCI R21 CA126137
Yue Cao, Ph.D.
Open positions for PostPost-Doctoral fellows
Departments of Radiation Oncology and Radiology
University of Michigan
– Send CV to yuecao@umich.edu
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Acknowledgement
Overall Goal
Establish, validate, and qualify quantitative
metrics/models for prediction of tumor and
NT response and outcome to radiation
therapy
Daniel Normolle,
Normolle, Ph.D.
Ted Lawrence, MD, Ph.D.
Randy Ten Haken,
Haken, Ph.D.
Physics
parameters
past
Clinical prognostic
factors
(Imaging)
Individual Predictive Model
biomarkers
future
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Functional Imaging For
Therapy Assessment
Example: Models
NTCP models
Identify biomarkers – sensitivity (Phase I/IIA)
– Dosimetric parameters
– Detect Tx effects on tumor or NT
Physics parameters (fixed variables)
TumorTumor- and organorgan-specific
Time for assesement
populationpopulation-based models
– Clinical variables (e.g., Dawson, 2002)
– too early: enough change?
– too late: loss an opportunity to rere-optimize Tx strategy
Primary vs metastatic tumors
subpopulationsubpopulation-based models
improve the predictive power of the model
Predict outcomes – specificity (Phase II)
– Biomarkers including those derived from imaging
Individual sensitivity or response to Tx (random variables)
individualized models
– Associate the biomarker with endpoints (e.g., clinical
outcomes)
Qualify quantitative metrics/models for clinical
decision making (Phase II/III)
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Why we need functional imaging
biomarkers? Example 1
Why we need functional imaging
biomarkers? Example 2
Brain Metastases
Risk for RILD
Causes of large error bars
Tumor response to
Tx is individualized
– binary scores of outcomes,
small number of events
– mean liver dose, although a
simple and useful predictor,
is lack of specificity
– Large variation in individual
patient sensitivity to
radiation
– Individual Patient
– Individual tumor
– Subtumor volume
Tumor size can not
predict outcome
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Pre-RT
2 wk of WBRT
1 month post RT
Functional imaging can
provide a means to evaluate
individual differences,
thereby to develop a model
to account for individual
variation
Dawson, et al, Red Journal, 2002
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Steps for Building a Response
Model with Imaging Biomarkers
HighHigh-Risk Subvolume in HGL
Phase I/IIa trial
An imaging biomarker
endpoint
e
gl
sin
st le
Te riab
va
w
re d
pa e
m lish s
o
C ta b b le
es ria
va
The imaging biomarker
Additional information
Combining biomarkers
and established variables
outcome
ild el
Bu mod
a
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Post-Gd T1 TV including
Core and CE rim
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Identify Predictive Variables
Single variable Cox regression analysis
S (t ) = S 0 (t ) , p = exp( Bx)
p
FLAIR TV
Vascular Leakage Volume
Estimated from DSC MRI
Cao, et al, Cancer Research, 2006
Joint Effects of Predictors
Multi variable Cox regression model
S (t ) = S 0 (t ) p , p = exp( B1 x1 + B2 x2 + ...)
Dependent
variable
OS
Tested independent variables
p
FLAIR tumor volume
n.s.
n.s.
Dependent
variable
Independent predictors
(xj)
Overall
p
OS
Post-Gd T1 tumor volume
n.s.
n.s.
OS
VKtrans (p=0.02)
Age (p=0.03)
0.009
TPS
VKtrans (p=0.03)
Ktrans (p=0.04)
Surgery (p=0.04)
0.003
OS
Contrast enhanced rim volume
n.s.
n.s.
OS
Vascular leakage volume (VKtrans)
0.02
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Vascular Normalization
Index
Vascular Normalization
Index
Patients: recurrent GBM
Therapy: cediranib,
cediranib, antianti-VEGF agent
Potential predictors
– Changes in Ktrans, CBV, and plasma collagen IV 1 d after
the first treatment
MultiMulti-variable Cox regression model
– VNI
Hypotheses:
(1) Anti-VEGF therapy can “normalize” brain tumor vasculature.
(2) The extent of vascular normalization will be predictive of
outcome of anti-VEGF therapy in GBM
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Normal Liver Injury after
Irradiation
VNI = −[ a∆ log K trans + b∆ log CBV + c∆ log collIV ]
– OS, p=0.006
– TPS, p=0.001
VNI predicts OS and TPS better than each of
individual predictors
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Early Changes in Venous Perfusion
170 ml/100g/min
RILD is a major limiting factor for intensifying
radiation treatment of hepatic cancer
Histopathology of RILD is venous occlusion
Hypothesis:
– Changes in portal venous perfusion during the early
course of RT has the potential to be a biomarker for
liver dysfunction after irradiation
– The perfusion biomarker has the potential to allow us
to select patients who are susceptible to liver injury
prior to clinical symptoms and therefore to modify
treatment
30Gy
20Gy
40Gy
10Gy
0 ml/100g/min
Prior to RT
After 45 Gy (during RT)
30 fx of 1.5 Gy/fx twice daily
Cao Y et al , Medical Physics 2007
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Dose Effect on Individuals
Dose Effect on Venous Perfusion
One month after RT
250
200
150
100
50
0
F p 1 m o n th a fte r R T
( m l/1 0 0 g /m in )
F p a f te r 3 0 F x
( m l/ 1 0 0 g /m in )
After 45 Gy (during RT)
1.6 ml/100g/miny per
GY + 129.3
= -0.016x
R = 0.47
p<0.0001
0
2000
4000
6000
dose at the time of scan (cGy)
8000
Linear regression model
2.5 ml/100g/min per GY
R = 0.77
200
p<0.0001
Fitn = α t + β t Ditn + eitn ,
250
150
Linear Mixed Model
100
Fitn = µ + α t + β t Ditn + ait + eitn ,
50
0
0
8000
2000
4000
6000
dose at the end of RT (cGy)
8000
Fitn = µ + α t + β it Ditn + ait + eitn,
Note: (1) time dependent slopes
(2) Individual intercepts and slopes
n: voxel or subregion, t: time, i: subject
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Individual Sensitivity to
Radiation
Liver Functional Volume
Individual patient
X-intercept
Pts
160
Fp 1 month after RT
Slope:
reduction in perfusion
caused by unit dose
Individual sensitivity
X-intercept :
critical dose resulting in
undetectable venous
perfusion
Individual sensitivity
Dose (Gy
(Gy))
Fp=0
Fp=0
LV%
Fp=0
Fp=0
1
NA
NA
NA
0%
2
-2.6
60
11
89%
3
-3.2
51
0
100%
4
-2.2
63
23
77%
5
-6.5
46
39
61%
6
-4.2
60
32
68%
7
-2.8
68
6
94%
0
8
-1.1
74
3
97%
Functional volume
9
-4.2
54
31
69%
10
-1.3
81
0
100%
-1.2
84
0
100%
140
120
Slope
FLV%
Fp>0
Fp>0
100
80
60
40
y = -0.0423x + 229.7
R2 = 0.85
20
0
0
1000
2000
3000
4000
5000
6000
dose at the end of RT (cGy)
Cao, et al, Int J Rad Onc Biol Phys, 2008
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mL/(100 g min)
per Gy
11
Substantially reduced
Venous Perfusion
130
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Overall Liver Function vs
Subunits of Functional Liver
1
n
N
Fitn = 1
n
n =1
N
n =1
( β it Dint + ait + α t )
5000
mean D at the end of RT
Fit =
Mean Liver Dose vs Liver
Functional Reserve
Venous perfusion at a subunit of
functional liver (>critical value)
Overall liver function
(ICG: functional reserve)
m ean F p pos t R T (F p>20)
NS
160
140
R2 = 0.89
120
P<0.001
100
R2 = 0.10
4500
NS
4000
3500
3000
2500
2000
80
60
2
4
6
8
10
12
T1/2 ICG post RT (min)
40
20
0
2
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6
8
10
12
T1/2 ICG post RT (min)
Neurotoxicity after Brain
Irradiation
Neurotoxicity after brain irradiation has
been drawn more attention
Neurocognitve dysfunctions manifest as
subcute and late declines in memory,
learning ability, and executive function
Two recent multicenter studies showed
postpost-RT neurocognitive dysfunction in
patients without tumor recurrence (Klein
2002 & Brown 2003)
– more prevalent in patients who had high total
doses, high fraction doses and/or large
irradiated volumes
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Complexity of Neurotoxicity
Multiple tissue compartments interaction
Radiological and histopathological signs
– Early vascular toxicity (e.g., bloodblood-brainbrain-barrier
disruption and vessel dilation)
– Subacute focal and diffuse demyelination
(depletion of glial precursors)
– Late structural degeneration (e.g., necrosis)
Functional imaging has the potential to
Identify early signs of neurotoxicity and thus
predict late neurocognitive dysfunction
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Dose and DoseDose-Volume Effects
on Neural Vasculature
0.0016
y = 7E-07x - 0.0005
R2 = 0.922
}
∆ Vp 3wk-preRT
0.0012
0.0008
0.0004
0.0000
-0.0004 0
500
1000
1500
2000
Error bars indicate
variation between
subjects
Dose and DoseDose-Volume Effects
Linear mixed model
Model 1: Dose effect
Fitn = α t + β t Ditn + ait + eitn ,
2500
Model 2: DoseDose-volume effect
-0.0008
-0.0012
Fitn = α t + β t ( D itn V d ) + a it + e itn ,
-0.0016
BioDose (cGy)
Linear Regression:
Fitn = α t + β t Ditn + eitn ,
βt (Slope): 0.7x10-2 (ml/100g)/Gy
d=40
d=40 Gy
Model 3: DoseDose-volume effect
Fitn = α t + β tVd + ait + eitn ,
Cao, et al, CCR, 2009
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Dose and DoseDose-Volume
Effects
Correlation Early Vascular Changes
with Late Neurocognitive Changes
βt
(p)
Model1
(dose effect)
0.0001
Model2
(dose(dose-volume effect)
0.0001
Model3
(dose(dose-volume effect)
r=0.61
2
1
0
-60
-10
40
-1
-2
-3
1
0
-100
-1
-50
0
50
∆ Vp% Left Te m poral 3 w k
15
r = 0.62
r = 0.73
2
1
0
-100
-1
-50
0
50
-2
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100
10
5
0
-150
-100
-50
0
50
100
150
-5
-10
∆ K% Left Frontal 3w
Model 2 indicates an interaction between dose and high-dose volume
100
-2
-3
3
n.s.
2
∆ Vp% Le ft Frontal 3 w k
-3
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r=0.67
Changes in Learning 6m
Vp
3
3
Changes in Recall 6 m
ait: individual intercept, an
individual offset from the
globe intercept, variance
significant
Time
Wk 3
Changes in Learning 6m
βt: globe slope, significant
Dependent
variable
Changes in Learning 6 m
Data published in
CCR 2009
∆ K% Left Tem poral 3w
Cao, et al, CCR, 2009
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Summary
PopulationPopulation-based models
– Physics or dosimetric parameters, fixed
variables
IndividualIndividual-based models
– Individual effects assessed by biomarkers
– Random variables
– linear mixed models have improved
statistical power compared to ANOVA or
linear regression models
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Summary
Limitations
– Limited data
– Limited signal to noise
– Limited numbers of patients
Cautions
– How many parameters can be fitted in the model
– Two separated data sets are needed for
developing and testing of the model
Suggestions
– Continual variables instead of binary variables
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