Estimating Cancer Risk Attributable to Computed
Tomography Coronary Angiography
Radiation safety and risk management
Estimating Cancer Risk Attributable to
Computed Tomography Coronary
Angiography
Spectacular technical developments in computed
tomography (CT), like 64-slice CT, dual-source CT
and 320-slice volume CT, led to fast growing
application of CT.
Koos Geleijns
Radiology department
Leiden University Medical Center
Leiden, The Netherlands
At the same time, concerns are expressed about
radiation exposure and associated radiation risks of
CT examinations.
Estimating Cancer Risk Attributable to Computed
Tomography Coronary Angiography
What do we need to know?
• Output of the scanner (CTDI, DLP)
• Organ dose (effective dose)
• Radiation (late) risk as a function of age and gender
(dependent on organ dose, age of exposure, gender)
• Competing risks (procedure and disease related
acute and late risks, false positives, false negatives)
• Decision model based on Disability Adjusted Life
Expectancy (DALE)
Estimating Cancer Risk Attributable to Computed
Tomography Coronary Angiography
Dosimetry
Radiation Risk Assessment
Comprehensive Risk Assessment
Justification: Balancing Risks and Benefits
Dosimetry, general considerations
• Operational dose quantities, easy
Estimating Cancer Risk Attributable to Computed
Tomography Coronary Angiography
• Measurable but also rather accurately provided on the operators
console
• Computed Tomography Dose Index (CTDI, mGy)
Dosimetry
Radiation Risk Assessment
Comprehensive Risk Assessment
Justification: Balancing Risks and Benefits
• Dose Length Product (DLP, mGy.cm)
• Dose quantities required for risk assessment, more difficult
• Not measurable! Should be measured in anthropomorphic
phantoms or calculated for (mathematical or voxel) phantoms
• Organ dose (HT, mSv)
• Effective dose (E, mSv)
Dosimetry, organ dose and effective dose assessment
Dosimetry, organ dose and effective dose assessment
Toshiba 64 slice, medium bow tie filter, measured nCTDIw 8.6 mGy/100 mAs
• Several software applications are available
• CT Expo
• WinDose
• ImPACT CT Patient Dosimetry Calculator
• The software may be inaccurate
• It does not accommodate patients of different sizes
• It is not validated for modern scanners
• It does not allow the off-center position of the patient in
cardiac CT
Note: Look up of nCTDIw for Aquilion 16 was 14.3 mGy/100mAs, measured 8.6 mGy/100mAs!
Calculation of organ dose for
the Toshiba Aquilion ONE
scanner (own research)
1x5
4x1
16 x 0.5
64 x 0.5
320 x 0.5
1998
2001
2004
2008
Dosimetry, organ dose and effective dose assessment
New scanners, e.g. Toshiba Aquilion ONE, 320 slice
Dosimetry, organ dose and effective dose assessment
CT scan
Entire trunk, male
Different sizes
Organ segmentation
Dose calculation
Dosimetry, organ dose and effective dose assessment
Dosimetry, k-factor of 0.017 mSv/(mGy.cm)
Core64: size and gender adepted acquisition protocols
• K-factor
• Simple assessment of effective dose from dose length product
• Defined for a general chest CT (120 kV) and a normal sized
patient, derived from dose characteristics of old, axial scanners
• The k-factor may be inaccurate in CT Coronary Angiography
• It was not derived for CT Coronary Angiography
• It does not accommodate patients of different sizes
• It is not validated for modern scanners
Dosimetry, organ dose and effective dose assessment
• The k-factor of 0.017 mSv/(mGy.cm) may be inaccurate in CT
Coronary Angiography
Estimating Cancer Risk Attributable to Computed
Tomography Coronary Angiography
• We derived higher k-factors for CTCA of respectively
• 0.030 mSv/(mGy.cm), 0.030 mSv/(mGy.cm) and 0.024
mSv/(mGy.cm) for small, normal and obese female voxel
phantoms;
• 0.023 mSv/(mGy.cm); 0.018 mSv/(mGy.cm) and 0.020
mSv/(mGy.cm) for small, normal and obese male voxel phantoms.
Dosimetry
Radiation Risk Assessment
Comprehensive Risk Assessment
Justification: Balancing Risks and Benefits
• The k-factor for chest CT yielded systematic and substantial
underestimation of effective dose.
Risk of radiation induced cancer – The Lancet
Risk of radiation induced cancer – The Lancet
International popular
press
The Lancet, 2004
Current risk estimations of radiation induced cancer
Example of a radiation risk model
• Methods for calculation of radiation induced mortality:
The BEIR VII ERR (excess relative risk) model
• One single effective dose dependent risk coefficient, e.g. 0.05
per Sv
ERR model: the excess radiation risk is expressed
relative to the background risk:
(s,a,b,d) = (s,a,b) [1 +
Better:
(s,a,b):
• Organ dose dependent risk coefficient from BEIR VII, as a
function of:
• Age and
• Gender
s
ERR(e,a)d]
background rate at zero dose, depends on
sex (s), attained age (a), and birth cohort (b).
s ERR(e,a): ERR unit of dose, depends on sex (s), age at
exposure (e), and attained age (a).
d:
dose
ERR(e,a):
exp ( e*) a , where e* is equal to e – 30
when e < 30, and equal to zero when e 30
Excess radiation risk is expressed relative to the
background risk
Example of a radiation risk model
The BEIR VII risk model, parameters s, and are
provided: ERR Model for Estimating Mortality from SiteSpecific Solid Cancer Mortality
leukemia mortality
all solid cancer mortality
Age-time patterns in radiation-associated risks for all solid cancer
mortality and leukemia mortality.
Curves are sex-averaged estimates of the risk at 1 Sv for people
exposed at age 10 (solid lines), age 20 (dashed lines), and age 30 or
more (dotted lines).
How to assess radiation risk, e.g. years life lost
Example of a radiation risk model, based on dose assessment
Dose assessment, organ dose and effective dose (mSv)
CCTA
CAG
Bone marrow
11.0
3.7
Stomach
6.9
2.3
Colon
0.1
0.0
Liver
10.0
3.3
Lung
58.0
19.3
Breast
69.0
23.0
Prostate
0.1
0.0
Uterus
0.1
0.0
Ovary
0.1
0.0
Bladder
0.0
0.0
Other solid cancers
15.0
5.0
Thyroid
0.9
0.3
Effective dose
15
5
Life tables that represent age and gender related
functions pertaining to mortality.
age
<1
1-2
2-3
3-4
4-5
5-6
6-7
7-8
8-9
9 - 10
10 - 11
…
…
70 - 71
71 - 72
72 - 73
73 - 74
74 - 75
…
…
ax
0.33
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
…
…
0.5
0.5
0.5
0.5
0.5
…
…
Mx
0.004551
0.000391
0.000252
0.000197
0.000167
0.00014
0.000148
0.000135
0.000129
0.000117
0.000129
…
…
0.086713
0.089787
0.093459
0.096347
0.100576
…
…
qx
0.004537
0.000391
0.000252
0.000197
0.000167
0.00014
0.000148
0.000135
0.000129
0.000117
0.000129
…
…
0.083109
0.085929
0.089286
0.091919
0.095761
…
…
px
0.995463
0.999609
0.999748
0.999803
0.999833
0.99986
0.999852
0.999865
0.999871
0.999883
0.999871
…
…
0.916891
0.914071
0.910714
0.908081
0.904239
…
…
lx
100000
99546.28
99507.37
99482.3
99462.7
99446.09
99432.17
99417.45
99404.03
99391.21
99379.58
…
…
40797.24
37406.61
34192.3
31139.39
28277.1
…
…
nd x
453.7165
38.91499
25.0727
19.59608
16.60888
13.92148
14.71487
13.42045
12.82229
11.62809
12.81914
…
…
3390.631
3214.313
3052.906
2862.293
2707.829
…
…
Lx
99696.01
99526.83
99494.83
99472.5
99454.4
99439.13
99424.81
99410.74
99397.62
99385.4
99373.17
…
…
39101.93
35799.46
32665.85
29708.25
26923.19
…
…
Tx
6786114
6686418
6586891
6487397
6387924
6288470
6189031
6089606
5990195
5890797
5791412
…
…
344851
305749
269949
237283
207575
…
…
ex
67.86114
67.16894
66.19501
65.21157
64.22432
63.23496
62.24374
61.25288
60.26109
59.2688
58.27567
…
…
8.452792
8.173653
7.89503
7.620038
7.34075
…
…
qx: Conditional probablity that an individual who has survived to start of the age interval
will die in the age interval.
How to assess radiation risk, e.g. years life lost
Mx
qx
px
lx
dx
Lx
Tx
ex
Life tables that represent age and gender related functions
pertaining to mortality.
In a cohort of 100000 males 61 radiation induced deaths, in a cohort of
100 000 females 147 radiation induced deaths.
Fraction of the age interval lived by those in the cohort population
who die in the interval.
Age-specific death rate.
Conditional probability that an individual who has survived to start
of the age interval will die in the age interval.
Conditional probability that an individual entering the age interval
will survive the age interval.
Life table cohort population.
Number of life table deaths in the age interval
Number of years lived during the age interval.
Cumulative number of years lived by the cohort population in the
age interval and all subsequent age intervals.
Life expectancy at the beginning of the age interval.
Radiation induced mortality is a late effect: reduction of life expectancy
3 days (males) and 9 days (females).
Survival, cohort of 100 000
ax
Survival, exposure to 15 mSv at age 40
80000
60000
Males, no radiation
Females, no radiation
Males, with radiation (15mSv)
Females, with radiation (15 mSv)
40000
20000
0
0
20
40
60
Age, years
Estimating Cancer Risk Attributable to Computed
Tomography Coronary Angiography
Dosimetry
Radiation Risk Assessment
Comprehensive Risk Assessment
Justification: Balancing Risks and Benefits
These data support
the calculation of
Disability Adjusted
Life Expactancy
(DALE).
100000
80
100
120
Current risk estimations of radiation induced cancer
Methods for calculation of radiation induced mortality:
Comparison of CCTA and CAG
Consider radiation exposure from CCTA (15 mSv) and CAG (5 mSv),
but also the fatal complications of diagnostic cardiac catheterization
• One single dose dependent risk coefficient, e.g. 0.05 per Sv
• Dose dependent risk coefficient as a function of:
• Age and/or
• Gender
• Suggested improvements, taking into account in the life tables:
• Disease related morbidity and mortality
• Other acute and late risks, e.g.:
• Imaging procedure related acute risks
• Risk of missing a diagnosis
Comprehensive Risk Assessment
de Bono D. Complications of diagnostic cardiac catheterisation: results from 34 041
patients in the United Kingdom confidential enquiry into cardiac catheter complications.
Br Heart J 1993; 70:297-300
Comprehensive Risk Assessment
• Invasive coronary angiography
(CAG)
30
• Assume organ doses that
correspond with an effective
dose of 5 mSv and the BEIR VII
organ dose specific late
radiation risks
0.11% acute CAG mortality
20
5 mSv CAG
15
10
5
25
• Assume organ doses that
correspond with an effective
dose of 15 mSv and the BEIR
VII organ dose specific late
radiation risks
15 mSv CCTA
20
Days life lost
25
Days life lost
• Non Invasive CT Coronary
Angiography (CTCA)
30
15
10
5
0
20
30
40
50
60
70
Age at CAG
80
90
100
• Assume an acute CAG
mortality risk of 0.11% *)
*) Noto TJ et al.. Cardiac catheterization 1990: a
report of the Registry of the Society for Cardiac
Angiography and Interventions (SCA&I). Cathet
Cardiovasc Diagn 1991 October;24(2):75-83.
0
20
30
40
50
60
70
Age at CCTA
80
90
100
• Assume no acute mortality risk
for non invasive CTCA
Comprehensive Risk Assessment
30
30
25
25
0.11% acute CAG mortality
15
15
10
10
5
5
0
Dosimetry
15 mSv CCTA
20
5 mSv CAG
Days life lost
Days life lost
20
Estimating Cancer Risk Attributable to Computed
Tomography Coronary Angiography
Radiation Risk Assessment
Comprehensive Risk Assessment
Justification: Balancing Risks and Benefits
0
20
30
40
50
60
70
Age at CAG
80
90
100
20
30
40
50
60
70
80
90
100
Age at CCTA
Medical decision making for symptom-based diagnosis
…a sign is an objective
symptom of a disease; a
symptom is a subjective sign
of disease…
Clinical decisions may be
supported by objective clinical
decision rules
Probability of CAD in % as function of age, gender and type of chest
pain
Age
Non-anginal
Atypical Angina
Typical Angina
Chest Pain
Year
Men
Women
Men
Women
Men
Women
30-39
5.2±0.8
0.8±0.3
21.8±2.4
4.2±1.3
40-49
14.1±1.3
2.8±0.7
46.1±1.8 13.3±2.9 87.3±1.0 55.2±6.5
50-59
21.5±1.7
8.4±1.2
58.9±1.5 32.4±3.0 92.0±0.6 79.4±2.4
60-69
28.1±1.9 18.6±1.9 67.1±1.3 54.4±2.4 94.3±0.4 90.6±1.0
69.7±3.2 25.8±6.6
Hamm CW, Goldmann BU, Heeschen C, Kreymann G, Berger J, Meinertz T. Emergency room triage of patients with acute chest pain by
means of rapid testing for cardiac troponin T or troponin I. N Engl J Med 1997; 337:1648-53.
Bayesian network for probability of CAD based on the most
important test results prior to imaging or intervention
Decision analysis is based
on the premise that humans
are reasonably capable of
framing a decision
problem, listing possible
decision options,
determining relevant
factors, and quantifying
uncertainty and
preferences, but are rather
weak in combining this
information into a
rational decision.
A Bayesian network, or belief
network, shows conditional
probability and causality
relationships between variables.
Probability of CAD, male 45 years old
Probability of CAD, male 45 years old
Chest pain
Troponin
Stress-ECG
Probability CAD
(%)
Atypical
Negative
Negative
2
Non anginal
Negative
Positive
4
Atypical
Negative
Equivocal
5
Typical
Negative
Negative
15
Atypical
Negative
Positive
16
Typical
Negative
Equivocal
32
Non anginal
Positive
Negative
35
Disability adjusted life expectancies (LE) used in the model
Chest pain
Troponin
Stress-ECG
Probability CAD (%)
Non anginal
Positive
Equivocal
58
Typical angina
Negative
Positive
61
Atypical angina
Positive
Negative
74
45
Male
45
Age Gender
Normal
LE
LE for
diagnosed
CAD
(years)
Reduction of
DALE due to
missed CAD
(years)
Reduction of
LE due to
false positive
(years)
33.5
22
1.04
0.27
Female
37.8
24
1.17
0.30
65
Male
16.3
11
0.51
0.13
65
Female
19.8
13
0.61
0.16
(years)
Non angina
Positive
Positive
83
Atypical angina
Positive
Equivocal
88
Typical angina
Positive
Negative
96
Atypical angina
Positive
Positive
96
Typical angina
Positive
Equivocal
98
Disability-adjusted life expectancies and years of life lost (YLL) based on
randomised trials.
Typical angina
Positive
Positive
99
Mukherjee D et al., Am Heart J 2002; The PRISM-PLUS Study Investigators. N Engl J Med 1998;
Anderson HV et al. J Am Coll Cardiol 1995
Excess mortality from imaging
Meta analysis: ROC 64-slice CT for detection of CAD
Age
Gender
YLL due to ICA
YLL due to
CCTA
45
Male
0.0387
0.0069
45
Female
0.0452
0.0108
65
Male
0.0192
0.0084
65
Female
0.0232
0.0045
Influence diagram to compute optimal policy
Range of clinical probability of CAD where CTCA is optimal as a
function of gender and age
End point DALE is
optimised as a function of
- age
Age
- gender and
- probability of CAD after
clinical evaluation
Yielding
- the optimal imaging policy
- way of handling uncertain
scans and
- diagnosis for each of these
patient groups
YLL = years of life lost
DALE = disability-adjusted life expectancy
Gender
Lower
bound (%)
Uncertain
negative
below (%)
Upper
bound (%)
45
Male
2
6
50
45
Female
2
6
50
65
Male
2
6
55
65
Female
2
6
50
Conclusion 1
Conclusion 2
• A preliminary study on the efficacy of CCTA for the
most important clinical CTCA indications has been
performed for males and females for the ages of 45
and 65.
• The model is based on meta-analysis results of
CCTA, average radiation dose and radiation risks.
• Outcomes in years of life lost (YLL) and disabilityadjusted life expectancy (DALE) were estimated.
• The study integrates cancer risk attributable to
CCTA in a medical decision making model
Thank you for your attention …
Ying Lie O
Alex Meijer
Job Kievit
Jaap Sont
Albert de Roos
Lucia Kroft
Safety and Efficacy in Computed Tomography: a broad perspective
EC-EURATOM 6 Framework Programme call 2003 Project no.
FP6/002388. 2005 – 2007.
• The current practice of the use of 40- and 64-slice CT
coronary angiography in suspected CAD seems to be justified
in patients with a low to intermediate probability (i.e. 2-50%
probability for CAD) after clinical evaluation.
• Age and gender have little effect on the range of clinical
probability where CTCA is optimal *)
• For very low probabilities (i.e. below 6% probability for CAD),
it seems most effective to assume non-diagnostic scans as
negative results.
*) Note: age and gender have a major impact on clinical probability
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Estimating Cancer Risk Attributable to Computed Tomography Coronary Tomography Coronary Angiography

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