Radiation safetyandrisk management

Estimating CancerRisk AttributabletoComputedTomographyCoronary

Angiography

Koos Geleijns

RadiologydepartmentLeidenUniversity MedicalCenter

Leiden,TheNetherlands

Estimating CancerRisk Attributableto ComputedTomography CoronaryAngiography

Spectacular technical developments in computed

tomography (CT), like 64-slice CT, dual-source CTand 320-slice volume CT, led to fast growing

application of CT.

At the same time, concerns are expressed aboutradiation exposure and associated radiation risks of

CT examinations.

EstimatingCancer Risk Attributableto ComputedTomographyCoronary 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 relatedacute and late risks, false positives, false negatives)

• Decision model based on Disability Adjusted Life

Expectancy (DALE)

Dosimetry

Radiati on Risk Assessment

Compr ehensive Risk Assessment

Justificat ion: Balan cing Risks and Benefits

Estimating Cancer Risk Attributable to ComputedTomography Coronary Angiography

Dosimetry

Radiation Risk Asse ssmen t

Compr ehensive Risk Ass essment

Just ifi cation: Balanc ing Risks and Benefits

Estimating Cancer Risk Attributable to ComputedTomography Coronary Angiography

Dosimetry, generalconsiderations

• Operational dose quantities, easy

• Measurable but also rather accurately provided on the operatorsconsole

• Computed Tomography Dose Index (CTDI, mGy)

• Dose Length Product (DLP, mGy.cm)

• Dose quantities required for risk assessment, more difficult

• Not measurable! Should be measured in anthropomorphicphantoms or calculated for (mathematical or voxel) phantoms

• Organ dose (HT, mSv)

• Effective dose (E, mSv)

Dosimetry, organdoseandeffectivedoseassessment

• 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 incardiac CT

Dosimetry, organdoseand effective dose assessmentToshiba64slice,mediumbowtie filter, measurednCTDIw 8.6 mGy/100 mAs

Note:Look up of nCTDIw for Aquilion 16 was14.3 mGy/100mAs, measured8.6 mGy/100mAs!

4 x 11 x 5 16 x 0.5 64 x 0.5 320x 0.5

1998 2001 2004 2008

Calculation of organ dose forthe Toshiba Aquilion ONEscanner (own research)

Dosimetry, organdoseandeffectivedoseassessment

New scanners,e.g.ToshibaAquilion ONE, 320slice

Dosimetry, organdoseand effective doseassessment

CT scan

Entire trunk, male

Differentsizes

Organ segmentation

Dosecalculation

Dosimetry, organdoseandeffectivedoseassessment

Core64: size and gender adepted acquisition protocols

Dosimetry, k-factor of 0.017mSv/(mGy.cm)

• K-factor

• Simple assessment of effective dose from dose length product

• Defined for a general chest CT (120 kV) and a normal sizedpatient, 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, organdoseandeffectivedoseassessment

• The k-factor of 0.017 mSv/(mGy.cm) may be inaccurate in CT

Coronary Angiography

• We derived higher k-factors for CTCA of respectively

• 0.030 mSv/(mGy.cm), 0.030 mSv/(mGy.cm) and 0.024mSv/(mGy.cm) for small, normal and obese female voxelphantoms;

• 0.023 mSv/(mGy.cm); 0.018 mSv/(mGy.cm) and 0.020mSv/(mGy.cm) for small, normal and obese male voxel phantoms.

• The k-factor for chest CT yielded systematic and substantial

underestimation of effective dose.

Dosimetry

Radiati on Risk Assessment

Compr ehensive Risk Assessment

Justificat ion: Balan cing Risks and Benefits

Estimating Cancer Risk Attributable to ComputedTomography Coronary Angiography

Riskof radiation inducedcancer – TheLancet

The Lancet, 2004

International popularpress

Risk of radiation inducedcancer– TheLancet

Currentrisk estimationsof radiation inducedcancer

• Methods for calculation of radiation induced mortality:

• One single effective dose dependent risk coefficient, e.g. 0.05

per Sv

Better:

• Organ dose dependent risk coefficient from BEIR VII, as a

function of:

• Age and

• Gender

Exampleof a radiation risk model

The BEIR VII ERR (excess relative risk) model

ERR model: the excess radiation risk is expressedrelative to the background risk:

λ(s,a,b,d) = λ(s,a,b) [1 + βs ERR(e,a)d]

λ(s,a,b): background rate at zero dose, depends onsex (s), attained age (a), and birth cohort (b).

βs ERR(e,a): ERR unit of dose, depends on sex (s), age atexposure (e), and attained age (a).

d: doseERR(e,a): exp (γ e*) aη, where e* is equal to e – 30

when e < 30, and equal to zero when e 30

Exampleof a radiation risk model

The BEIR VII risk model, parameters βs, γ and η areprovided: ERR Model for Estimating Mortality from Site-Specific Solid Cancer Mortality

Excessradiation risk is expressedrelativeto thebackground risk

Age-time patternsin radiation-associatedrisksfor all solid cancermortality andleukemia mortality.Curvesare sex-averagedestimatesof therisk at 1 Sv for peopleexposedat age10 (solid lines),age20 (dashedlines),andage30 ormore (dottedlines).

all solid cancer mortality leukemia mortality

Exampleof a radiation risk model, basedon doseassessment

Dose assessment, organ dose and effective dose (mSv)

CCTA CAG

Bone marro w 11.0 3.7

Stomach 6.9 2.3

Colo n 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

How to assessradiation risk, e.g.yearslife lost

Life tables that represent age and gender relatedfunctions pertaining to mortality.

age a x M x q x p x l x nd x L x T x e x

<1 0.33 0.004551 0.004537 0.995463 100000 453.7165 99696.01 6786114 67.861141 - 2 0.5 0.000391 0.000391 0.999609 99546.28 38.91499 99526.83 6686418 67.168942 - 3 0.5 0.000252 0.000252 0.999748 99507.37 25.0727 99494.83 6586891 66.195013 - 4 0.5 0.000197 0.000197 0.999803 99482.3 19.59608 99472.5 6487397 65.211574 - 5 0.5 0.000167 0.000167 0.999833 99462.7 16.60888 99454.4 6387924 64.224325 - 6 0.5 0.00014 0.00014 0.99986 99446.09 13.92148 99439.13 6288470 63.234966 - 7 0.5 0.000148 0.000148 0.999852 99432.17 14.71487 99424.81 6189031 62.243747 - 8 0.5 0.000135 0.000135 0.999865 99417.45 13.42045 99410.74 6089606 61.252888 - 9 0.5 0.000129 0.000129 0.999871 99404.03 12.82229 99397.62 5990195 60.26109

9 - 10 0.5 0.000117 0.000117 0.999883 99391.21 11.62809 99385.4 5890797 59.268810 - 11 0.5 0.000129 0.000129 0.999871 99379.58 12.81914 99373.17 5791412 58.27567

… … … … … … … … … …… … … … … … … … … …

70 - 71 0.5 0.086713 0.083109 0.916891 40797.24 3390.631 39101.93 344851 8.45279271 - 72 0.5 0.089787 0.085929 0.914071 37406.61 3214.313 35799.46 305749 8.17365372 - 73 0.5 0.093459 0.089286 0.910714 34192.3 3052.906 32665.85 269949 7.8950373 - 74 0.5 0.096347 0.091919 0.908081 31139.39 2862.293 29708.25 237283 7.62003874 - 75 0.5 0.100576 0.095761 0.904239 28277.1 2707.829 26923.19 207575 7.34075

… … … … … … … … … …… … … … … … … … … …

qx: Conditionalprobablitythatanindividual who hassurvivedto start of theageintervalwill die in theageinterval.

How to assessradiation risk, e.g. yearslife lost

Life tables that represent age and gender related functionspertaining to mortality.

ax Fraction of the age interval lived by those in the cohort populationwho die in the interval.

Mx Age-specific death rate.qx Conditional probability that an individual who has survived to start

of the age interval will die in the age interval.px Conditional probability that an individual entering the age interval

will survive the age interval.lx Life table cohort population.dx Number of life table deaths in the age intervalLx Number of years lived during the age interval.Tx Cumulative number of years lived by the cohort population in the

age interval and all subsequent age intervals.ex Life expectancy at the beginning of the age interval.

Survival, exposureto 15 mSvat age40

0

20000

40000

60000

80000

100000

0 20 40 60 80 100 120

Age, years

Su

rviv

al,c

oh

ort

of

100

000

Males, no radiationFemales, no radiation

Males, with radiation (15mSv)

Females, with radiation (15 mSv)

In a cohort of 100000males61 radiationinduceddeaths,in a cohort of100 000females147radiationinduceddeaths.

Radiationinducedmortality is a late effect: reductionof life expectancy3 days (males)and9 days(females).

ThesedatasupportthecalculationofDisability AdjustedLife Expactancy(DALE).

Dosimetry

Radiation Risk Asse ssmen t

Compr ehensive Risk Ass essment

Just ifi cation: Balanc ing Risks and Benefits

Estimating Cancer Risk Attributable to ComputedTomography Coronary Angiography

Currentrisk estimationsof radiation inducedcancer

Methods for calculation of radiation induced mortality:

• 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

Comparisonof CCTAandCAG

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

Consider radiation exposure from CCTA (15 mSv) and CAG (5 mSv),but also the fatal complications of diagnostic cardiac catheterization

ComprehensiveRiskAssessment

• Invasive coronary angiography(CAG)

• Assume organ doses thatcorrespond with an effectivedose of 5 mSv and the BEIR VIIorgan dose specific lateradiation risks

• Assume an acute CAG

mortality risk of 0.11% *)

0

5

10

15

20

25

30

20 30 40 50 60 70 80 90 100

Age at CAG

Da

yslif

elo

st

0.11% acute CAG mortality

5 mSv CAG

* ) Noto TJ et al.. Cardiac catheterization1990: a

reportof theRegistry of theSocietyfor Cardiac

AngiographyandInterventions(SCA&I). Cathet

CardiovascDiagn 1991 October;24(2):75-83.

0

5

10

15

20

25

30

20 30 40 50 60 70 80 90 100

Age at CCTA

Day

slif

elo

st

15 mSv CCTA

ComprehensiveRisk Assessment

• Non Invasive CT CoronaryAngiography (CTCA)

• Assume organ doses thatcorrespond with an effectivedose of 15 mSv and the BEIRVII organ dose specific lateradiation risks

• Assume no acute mortality risk

for non invasive CTCA

ComprehensiveRiskAssessment

0

5

10

15

20

25

30

20 30 40 50 60 70 80 90 100

Age at CCTA

Da

yslif

elo

st

15 mSv CCTA

0

5

10

15

20

25

30

20 30 40 50 60 70 80 90 100

Age at CAG

Da

yslif

elo

st

0.11% acute CAG mortality

5 mSv CAG Dosimetry

Radiati on Risk Assessment

Compr ehensive Risk Assessment

Justificat ion: Balan cing Risks and Benefits

Estimating Cancer Risk Attributable to ComputedTomography Coronary Angiography

Medical decisionmaking for symptom-based diagnosis

…a signis an objectivesymptomof a disease;asymptomis a subjective signof disease…

Clinical decisionsmaybesupportedby objectiveclinicaldecisionrules

Probability of CAD in % asfunction of age,gender and type of chestpain

Hamm CW, Goldmann BU, Heeschen C, Kreymann G, Berger J, Meinertz T. Emergency room triage of patients with acute chest pain bymeans of rapid testing for cardiac troponin T or troponin I. N Engl J Med 1997; 337:1648-53.

90.6±1.094.3±0.454.4±2.467.1±1.318.6±1.928.1±1.960-69

79.4±2.492.0±0.632.4±3.058.9±1.58.4±1.221.5±1.750-59

55.2±6.587.3±1.013.3±2.946.1±1.82.8±0.714.1±1.340-49

25.8±6.669.7±3.24.2±1.321.8±2.40.8±0.35.2±0.830-39

WomenMenWomenMenWomenMenYear

Typical AnginaAtypical AnginaNon-anginal

Chest Pain

Age

Bayesiannetwork for probability of CAD basedon the mostimportant test resultsprior to imaging or intervention

Decision analysisis basedon thepremisethathumansare reasonablycapableoffr aming a decisionproblem, listing possibledecisionoptions,determiningrelevantfactors, and quantifyinguncertaintyandpreferences,but are ratherweak in combining thisinfor mation into arational decision.

A Bayesiannetwork, or beliefnetwork, showsconditionalprobability andcausalityrelationships betweenvariables.

Probability of CAD,male45 yearsold

35NegativePositiveNon anginal

32EquivocalNegativeTypical

16PositiveNegativeAtypical

15NegativeNegativeTypical

5EquivocalNegativeAtypical

4PositiveNegativeNon anginal

2NegativeNegativeAtypical

Probability CAD(%)

Stress-ECGTroponinChest pain

Probability of CAD, male45 yearsold

96PositivePositiveAtypical angina

98EquivocalPositiveTypical angina

99PositivePositiveTypical angina

96NegativePositiveTypical angina

88EquivocalPositiveAtypical angina

83PositivePositiveNon angina

74NegativePositiveAtypical angina

61PositiveNegativeTypical angina

58EquivocalPositiveNon anginal

Probability CAD (%)Stress-ECGTroponinChest pain

Disabili ty adjustedlife expectancies (LE) usedin themodel

0.160.611319.8Female65

0.130.511116.3Male65

0.301.172437.8Female45

0.271.042233.5Male45

Reduction ofLE due to

false positive(years)

Reduction ofDALE due tomissed CAD

(years)

LE fordiagnosed

CAD(years)

NormalLE

(years)

GenderAge

Disability-adjustedlife expectancies and yearsof life lost(YLL) basedonrandomisedtrials.MukherjeeD et al., Am HeartJ 2002;The PRISM-PLUS StudyInvestigators.N Engl J Med 1998;AndersonHV et al. J Am Coll Cardiol1995

Excessmortality from imaging

0.00450.0232Female65

0.00840.0192Male65

0.01080.0452Female45

0.00690.0387Male45

YLL due toCCTA

YLL due to ICAGenderAge

Metaanalysis: ROC64-sliceCT for detection of CAD

Influencediagramto computeoptimalpolicy

YLL = yearsof life lostDALE = disability-adjustedlife expectancy

End point DALE isoptimisedasa functionof- age- genderand- probability of CAD afterclinical evaluation

Yielding- theoptimal imagingpolicy- way of handlinguncertainscans and- diagnosisfor eachof thesepatient groups

Rangeof clinical probability of CAD whereCTCAis optimal as afunction of gender andage

5062Female65

5562Male65

5062Female45

5062Male45

Upperbound (%)

Uncertainnegative

below (%)

Lowerbound (%)

GenderAge

Conclusion1

• A preliminary study on the efficacy of CCTA for themost important clinical CTCA indications has been

performed for males and females for the ages of 45and 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 disability-adjusted life expectancy (DALE) were estimated.

• The study integrates cancer risk attributable to

CCTA in a medical decision making model

Conclusion2

• 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:ageandgenderhavea major impact on clinical probability

Thank you for your attention …

Ying Li e O

Alex Meijer

Job Kievit

JaapSont

Albert de Roos

Lucia Krof tSafety and Efficacy in Computed Tomography: a broad perspectiveEC-EURATOM 6 Framework Programme call 2003 Project no.FP6/002388.2005– 2007.