8/16/2011
Methodological issues
Methodological issues and key
results in the QUANTEC 2010
review of quantitative analysis of
normal tissue effects in the clinic
(The QUANTEC steering committee:)
Soeren Bentzen, Sandy Constine, Joe
Deasy, Avi Eisbruch, Andrew Jackson, Larry
Marks, Randy Ten Haken, Ellen Yorke
1.
2.
3.
4.
5.
6.
7.
Avoiding xerostomia (‘dry-mouth
syndrome’) in H&N patients
A sample of key results
• Xerostomia and salivary
parotid gland irradiation
• Late rectal bleeding
• Radiation pneumonitis
and lung irradiation
• Erectile dysfunction and
penile bulb irradiation
No consensus on best NTCP models (e.g.,
simple vs. large model)
Results not stated in comparable terms
(e.g., NTCP model vs. DVH thresholds)
Sometimes the ‘target’ tissues aren’t known
Endpoint definitions can be questionable
Results depend on typical dose
distributions, which are evolving
Implicit assumption of organ uniformity
(ignoring stem cell rescue, etc.)
Assumption that spatial dose pattern
doesn’t matter
“OK”
“Worse”
• Salivary function is stimulated or unstimulated
• Stimulated flow is dominated by parotid gland
contributions
• Unstimulated consists of contributions from
submandibular salivary glands and multiple
smaller glands
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8/16/2011
(In press, IJROBP)
(Blanco et al. IJROBP 2005)
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8/16/2011
Test of QUANTEC “20/20” recommendation
No Grade 4 xerostomia at 3 months
Grade 4 xerostomia at 3 months
Predictor
70
Sensitivity: 23/27=85%
Specificity: 19/23=83%
LP mean dose, Gy
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
RP mean dose, Gy
(In press, IJROBP, Vitali Moiseenko, John Wu, et al., BCCA)
Radiotherapy alone
PROs scores correlation with the mean dose to
contralateral parotid gland
XEROSTOMIA PATIENT REPORTED OUTCOMES
ARE A LINEAR FUNCTION OF CONTRALATERAL
PAROTID GLAND MEAN DOSE
CHEWING
SWALLOWING
DRYNESS
Ziad H Saleh1, Wade Thorstad2 , Aditya Apte1 , and Joseph O.
Deasy1
1- Memorial Sloan-Kettering Cancer Center (Department of
Medical Physics) , New York , USA
2 -Washington University School of Medicine (Department of
Radiation Oncology) , St. Louis , USA
8/16/2011
ESTRO 2011
Slope = 0.93
Slope = 1.41
Slope = 1.26
%/Gy
%/Gy
%/Gy
PROs
increases linearly
as a function of the
mean dose to
the contralateral mean dose
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ESTRO 2011
12
3
8/16/2011
Induction + Concurrent ChemoRT
PROs scores correlation with the mean dose to
Contralateral parotid gland
CHEWING
SWALLOWING
DRYNESS
Slope = 2.04
Slope = 0.66
Slope = 2.07
%/Gy
%/Gy
%/Gy
PROs response as a linear function is steeper compared
to RT alone
8/16/2011
ESTRO 2011
13
Continued benefit
to patient by reducing mean
dose
Plus:
+
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
g
E
U
D

d

d

dd

.
.
.

/
N


123
n
V
o
x
e
l
s


a
a
a
a
1
/
a
Results in different relative contributions
to gEUD within the DVH:
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8/16/2011
e s tim a te s o f L K B vo lu m e e ffe c t p a ra m e te r n
fo r re c ta l c o m p lic a tio n s
Are IMRT rectal dose-volume limits a
problem?
R T O G 9 4 -0 6
Typical 3DCRT plan (sagittal)
Typical IMRT plan (sagittal)
S tu d y
Tucker
R a n c a ti
P e e te rs
Sohn
0 .0 1
0 .1
1
v o lu m e e ffe c t p a ra m e te r, n
(Slide courtesy A. Jackson et al.)
(Note: a = 1/n)
Rectal gEUD = 53 Gy
(a= 10)
Rectal gEUD = 24 Gy
Variation in rectal DVHs for daily Fx’s
(Rad Oncol 2005)
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Purpose/background:
• Validating a predictive model for late rectal
bleeding would enable safer treatments or
dose escalation.
• We tested the normal tissue complication
probability (NTCP) model recommended in
the recent QUANTEC review (Michalski et al.,
IJROBP, 2010 (76) S123-9).
Materials/Methods:
• 161 prostate cancer patients were treated with 3D
conformal radiotherapy for prostate cancer at the
British Columbia Cancer Agency in a prospective
protocol.
• Total prescription dose for all patients was 74 Gy,
delivered in 2 Gy/fraction.
• Rectal dose volume histograms were extracted and
fitted to a Lyman-Kutcher-Burman NTCP model. Two
treatment plans could not be extracted.
• Late rectal bleeding (>=grade 2) was observed in
12/159 patients (7.5 %).
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8/16/2011
Results:
• Multivariate logistic regression with dose-volume
parameters (V50, V60, V70, etc.) was non-significant.
• Among clinical variables, only age was significant on a
Kaplan-Meier log-rank test (p = 0.007, with an optimal
cut point of 77 years).
• Best-fit Lyman-Kutcher-Burman model parameters
(with 95% confidence intervals) were: n = 0.068 (0.01,
+infinity); m = 0.14 (0.0, 0.86); and TD50 = 81 (27, 136)
Gy.
• The peak values fall within the 95% QUANTEC
confidence intervals.
Results: On this dateset, both models had
only modest ability to predict
complications:
• the best-fit model had a Spearman’s rank
correlation coefficient of rs=0.099 (p=0.11)
and AUC of 0.62;
• the QUANTEC model had rs=0.096 (p=0.11)
and a corresponding AUC of 0.61.
• Although the QUANTEC model consistently
predicted higher NTCP values, it could not be
rejected according to the chi-square test
(p=0.44).
Int. J. Radiation Oncology Biol. Phys., Vol. 78, No. 4, pp.
1253–1260, 2010
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8/16/2011
Tucker et al., Int. J. Radiation Oncology Biol. Phys., Vol. 78, No.
4, pp. 1253–1260, 2010
Identifying the most influential
tissues
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8/16/2011
Trismus in 480 H&N pts., MSKCC
(S. Rao, Z. Saleh et al.)
Which tissues are most influential?
Correlation
map shows
hotspot just
outside of
contoured
muscles –
surprise!
(Ziad Saleh et al., poster this meeting)
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8/16/2011
P n e u m o n itis , m e a n d o s e re s p o n s e - w h o le lu n g
P ro b a b ility o f p n e u m o n itis
1 .0
M S K C C (1 0 /7 8 )
D u k e (3 9 /2 0 1 )
M ic h ig a n (1 7 /1 0 9 )
M D A n d e rs o n (~ 4 9 ? /2 2 3 )
N K I (1 7 /1 0 6 )
W U (5 2 /2 1 9 )
M a rte l e t a l. (9 /4 2 )
O e z te l e t a l. (1 0 /6 6 )
R a n c a ti e t a l. (7 /5 5 )
K im e t a l. (1 2 /6 8 )
lo g is tic fit
0 .9
0 .8
0 .7
0 .6
0 .5
0 .4
WUSTL RP dataset
• 228 patients with non-small cell lung cancer (NSCLC)
treated definitively with radiation +/- chemotherapy
between 1991-2001
• 48 cases of RP (steroids or more intensive
intervention)
• 3D treatment plan archives available
0 .3
0 .2
– Non-heterogeneity corrected dose distributions
0 .1
0 .0
0
10
20
30
M e a n d o s e (G y )
• Minimum six months follow-up post-treatment
unless patient developed pneumonitis < 6 mos.
Consistent – but not very predictive.
Tumor location is associated with risk of
pneumonitis
Superior/Inferior Position
1
Superior 25%:
15.9% (7/44)
0.8
upper-mid 25%:
30.2% (42/139)
0.6
0.4
0.2
0
0
Inferior 50%:
44.4% (20/45)
Right Lung:
47/137 (34.3%)
0.2
Left Lung:
22/91 (24.2%)
0.4
0.6
Right/Left Position
0.8
Multi-variate modeling of combined
WUSTL and RTOG 93-11 datasets (Bradley
et al. IJROBP 2007)
• Chosen from many candidate
models; logistic function of:
1
.
5
+
0
.
1
1

M
e
a
n
L
u
n
g
D
o
s
e
2
.
8

P
o
s
S
u
p
I
n
f
• Spearman’s rank correlation
coefficient 0.3 (on cross
validation data)
1
(Hope et al, IJROBP 2007)
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Dataset
• Heart volumes of WUSTL archived plans were re-contoured
within CERR by a single physician (n = 209, with 48 RP events).
• Heart and normal lung (lung minus gross tumor volume) dosevolume parameters were extracted for further modeling using
CERR.
• Evaluated factors included:
– clinical (age, gender, race, performance status, weight loss, smoking,
histology)
– dosimetric parameters for heart and normal lungs (D5-D100, V10-V80,
mean dose, maximum dose, and minimum dose)
– treatment factors (chemotherapy, treatment time, fraction size)
– location parameters (heart center-of-dose, sup-inf within the heart;
and center-of-target mass within the normal lungs.)
Highest
univariate
correlations
Variable
Spearman Corr.
Significance
D5_Heart
0.256
<0.0002
D10_Heart
0.24
<0.0003
V70_heart
0.239
<0.0003
gEUD_Heart
(a=10)
0.249
<0.0001
Maximum Heart
Dose
0.227
<0.0006
Superior -Inferior
position of GTV
0.219
<0.0008
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Are simple NTCP models
sophisticated enough?
(2010)
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Conclusion
• Heart irradiation may be an important risk factor
for radiation pneumonitis, as previously seen in
animal studies (Luijk et al., IJROBP 2007).
• In this single-institution dataset, RP is better
associated with high-dose heart irradiation factors
than previously reported lung dosimetry factors.
• The lung ‘location effect’ was not selected in
multivariate modeling when heart factors were
included.
• Obviously, these observations need to be tested
on independent datasets.
Another validation study: acute
esophagitis (dysphagia)
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Nomogram to predict dysphagia
Radiation-induced dysphagia: multivariate analysis
Table 1. Odds ratio's for developing dysphagia
Variable
Coefficient
Odds ratio
Age
-0.03
0.97
Gender
male
ref
female
0.50
1.65
WHO-PS
0-1
ref
>=2
0.57
1.76
Chemotherapy
no/sequential
ref
concurrent
0.93
2.53
MED
0.06
1.06
MAXED
0.03
1.03
OTT
-0.06
0.94
95% CI
0.95 - 0.99
P
0.003
0.011
1.12 - 2.43
0.012
1.13 - 2.75
<0.001
1.64
1.04
1.01
0.92
-
3.91
1.09
1.05
0.96
<0.001
0.0002
<0.001
C-statistic = 0.77 (bootstrap)
C. Oberije et al, Radiotherapy and Oncology, 2010
C. Oberije et al, Radiotherapy and Oncology, 2010
External validation
Washington University/Mallinckrodt data (n=216)
1.0
≥ Grade 2 (n=85)
AUC=0.75
0.6
0.4
Ideal
Logistic calibration
Nonparametric
Grouped observations
Ideal
Logistic calibration
Nonparametric
Grouped observations
0.0
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
Actual Probability
0.8
≥ Grade 3 (n=22)
AUC=0.77
0.2
Actual Probability
0.8
1.0
External validation
Washington University/Mallinckrodt data (n=216)
0.0
0.2
0.4
0.6
0.8
1.0
Predicted Probability
1.0
Predicted Probability
C. Oberije et al, Radiotherapy and Oncology, 2010
C. Oberije et al, Radiotherapy and Oncology, 2010
Courtesy of J. Deasy, J. Bradley, I. El Naqa, E. Huang
Courtesy of J. Deasy, J. Bradley, I. El Naqa, E. Huang
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8/16/2011
External validation Ghent data (n=100)
External validation Ghent data (n=100)
≥ Grade 3 (n=8)
≥ Grade 2 (n=19)
AUC=0.94
AUC=0.86
C. Oberije et al, Radiotherapy and Oncology, 2010
Courtesy of J. van Meerbeeck, K. Vandecasteele, W. De Neve
C. Oberije et al, Radiotherapy and Oncology, 2010
Courtesy of J. van Meerbeeck, K. Vandecasteele, W. De Neve
Methodological issues
1.
2.
3.
4.
5.
6.
7.
No consensus on best NTCP models (e.g.,
simple vs. large model)
Results not stated in comparable terms
(e.g., NTCP model vs. DVH thresholds)
Sometimes the ‘target’ tissues aren’t known
Endpoint definitions can be questionable
Results depend on typical dose
distributions, which are evolving
Implicit assumption of organ uniformity
(ignoring stem cell rescue, etc.)
Assumption that spatial dose pattern
doesn’t matter
16