JNCI J Natl Cancer Inst 2012 Wen 1599 611

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DOI:10.1093/jnci/djs372

JNCI | Articles 1599

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Article

Hpaoua canoma rsk Pdon Mod fo h Gna

Popuaon: th Pdv Pow of tansamnass

Chi-Pang Wen*, Jie Lin*, Yi Chen Yang, Min Kuang Tsai, Chwen Keng Tsao, Carol Etzel, Maosheng Huang, Chung Yi Hsu,

Yuanqing Ye, Lopa Mishra, Ernest Hawk, Xifeng Wu

Manuscript received March 01, 2012; revised July 26, 2012; accepted July 27, 2012

*Authors contributed equally to this work.

Correspondence to: Xifeng Wu, MD, PhD, Department of Epidemiology, The University of Texas MD Anderson Cancer Center, 1155 Herman P. Pressler

Blvd, Unit 1340, Houston, TX 77030 (e-mail: [email protected]).

Background Risk prediction models for hepatocellular carcinoma are available for individuals with chronic hepatitis B virus

(HBV) and hepatitis C virus (HCV) infections who are at high risk but not for the general population with average

or unknown risk. We developed five simple risk prediction models based on clinically available data from the

general population.

Methods A prospective cohort of 428 584 subjects from a private health screening firm in Taiwan was divided into two

subgroupsone with known HCV test results (n = 130 533 subjects) and the other without (n = 298 051 subjects).

A total of 1668 incident hepatocellular carcinomas occurred during an average follow-up of 8.5 years. Model

inputs included age, sex, health historyrelated variables; HBV or HCV infectionrelated variables; serum levels of

alanine transaminase (ALT), aspartate transaminase (AST), and alfa-fetoprotein (AFP), as well as other variables

of routine blood panels for liver function. Cox proportional hazards regression method was used to identify risk

predictors of hepatocellular carcinoma. Receiver operating characteristic curves were used to assess discrimina-

tory accuracy of the models. Models were internally validated. All statistical tests were two-sided.

Results Age, sex, health history, HBV and HCV status, and serum ALT, AST, AFP levels were statistically significant inde-

pendent predictors of hepatocellular carcinoma risk (all P < .05). Use of serum transaminases only in a model

showed a higher discrimination compared with HBV or HCV only (for transaminases, area under the curve

[AUC] = 0.912, 95% confidence interval [CI] = 0.909 to 0.915; for HBV, AUC = 0.840, 95% CI = 0.833 to 0.848; and for

HCV, AUC = 0.841, 95% CI = 0.834 to 0.847). Adding HBV and HCV data to the transaminase-only model improved

the discrimination (AUC = 0.933, 95% CI = 0.929 to 0.949). Internal validation showed high discriminatory accuracy

and calibration of these models.

Conclusion Models with transaminase data were best able to predict hepatocellular carcinoma risk even among subjects with

unknown or HBV- or HCV-negative infection status.

J Natl Cancer Inst 2012; 104:15991611

Chronic hepatitis B virus (HBV) and hepatitis C virus (HCV)

infections are three to five times more common than HIV infection

and AIDS in the United States, pacing those infected with HBVor HCV at increased risk for hepatoceuar carcinoma, cirrhosis,

and death (1). However, unike HIV infection and AIDS, a recent

Institute of Medicine (IOM) report noted that most of the five

miion Americans with HBV or HCV infections are unaware

of their risks unti they deveop symptoms of hepatoceuar

carcinoma or cirrhosis (2). Many of the 150 000 deaths expected

in the next 10 years coud be prevented if physicians and the

pubic were better educated about eary recognition of these

conditions.

Athough HBV or HCV carriers are at increased risk of hepa-

toceuar carcinoma, the cancer aso occurs among noncarriers

of these viruses (3,4). In this aspect, cinicians wi have difficuty

assessing this ow-risk popuation. To start checking for carrier

status, HBV or HCV testing wi require extra effort and extracost; however, recommendations for universa screening have been

suggested (5). Even when HBV or HCV testing is performed and

found to be positive, the majority of carriers do not take action

to reduce their risk (2). This is party because of the fact that the

reationship between hepatitis carrier status and hepatoceuar car-

cinoma risk, specific to the individua, is not readiy avaiabe to

the doctors. As a resut, the test information, whether positive or

negative for HBV or HCV, is often wasted. As not knowing ones

risk is a major barrier for taking action (2), much of the recent

progress made on the treatment of HBV or HCV cannot be fuy

utiized (6).
mailto:[email protected]?subject=http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-mailto:[email protected]?subject=


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Vol. 104, Issue 20 | October 17, 20121600 Articles | JNCI

Avaiabe prediction modes for hepatoceuar carcinoma are

imited to individuas at eevated risk who are carrying HBV (79)

or HCV (10,11). Many of these modes aso need additiona infor-

mation from cinica workups, such as presence of HBV DNA

in the bood (7,8), and this has further imited the appicabiity

of the prediction mode because such measures are usuay not

readiy avaiabe in cinica settings. As much of the pubic is una-

ware of their risk profie, the need for a new average-risk mode

is obvious. Finay, assessing risk in the genera pubic withoutmass screening for HBV or HCV is another chaenge, because

conducting such screenings has not been proven effective scien-

tificay (5). Hepatoceuar carcinoma has a high mortaity rate,

and possibe interventions, such as interferon therapy or ifestye

changes, are avaiabe to reduce the mortaity or ater the course

of the disease, provided individuas at high risk can be identified.

A simpe, easy-to-administer risk prediction mode based on com-

mony avaiabe data at heath checkups woud be of great vaue.

Taking advantage of a medica screening program invoving

neary haf a miion heathy individuas in Taiwan with foow-up

data (12,13), we deveoped a prediction mode for hepatoceuar

carcinoma based on data routiney coected in a typica office visit.The intent of the prediction mode is to provide a simpe, efficient,

and widey avaiabe too to identify and quantify cancer risk in the

average-risk popuation.

Mhods

Study Population and Data Collection

The study popuation was obtained from a standard medica

screening program conducted by the MJ Heath Management

Institution (MJ). From 1994 to 2008, a tota of 428 584 subjects,

free of cancer at baseine, were recruited. In the MJ cohort, because

tests for HCV infection were performed at an extra cost to mem-

bers, ony a subset of 130 533 participants has data on HCV status.Given the importance of HCV as a risk factor for hepatoceuar

carcinoma, we divided the cohort into two subcohorts in order to

provide more accurate risk prediction estimates: one cohort had

the HCV test (n = 130 533 subjects), and the other had no HCV

test (n = 298 051 subjects). Participants in the MJ cohort were aged

20 years or oder.

A participants competed a sef-administered questionnaire

covering demographic characteristics and heath history, incud-

ing ifestye and medica history (such as diabetes, hypertension,

stroke, heart diseases). Subjects who sef-reported having been

diagnosed with diabetes or currenty taking diabetes medica-

tion were defined as having diabetes. A subjects went throughtesting for anthropometric measurements (eg, height, weight,

waist circumference, hip circumference, body fat percentage,

etc.), bood pressure, puse rate, respiration rate, and chest cir-

cumference. Overnight fasting bood was anayzed for a stand-

ard pane, incuding hemogram, bood sugar tests, iver function

tests, rena function tests, bood ipid tests, thyroid function tests,

bood grouping, the presence of HBV surface antigen in bood

(HBsAg), and the presence of HCV antibody in bood (offered

to a subgroup of members with additiona cost). Individuas who

tested positive for HBsAg are referred to as HBV+ subjects and

those tested positive for HCV antibody are referred to as HCV+

subjects, and individuas who tested negative were referred to as

HBV and HCV subjects, respectivey. Smoking was cas-

sified by the number of pack-years (ie, daiy cigarette quantity

duration in years) among ever-smokers. Acoho consumption was

cassified into reguar drinkers (those who consumed 2 drinks/

day on 3 days/week) and occasiona drinkers (those who con-

sumed


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JNCI | Articles 1601jnci.oxfordjournals.org

Four modes were deveoped in the subcohort without an

HCV test (Tabe 1), and a fifth mode was added in the subcohort

with an HCV test (Tabe 2). Modeing started with heath history

(mode 1: age, sex, pack-year of smoking, acoho drinking, physi-

ca activity, and diabetes) or transaminase ony (mode 2: age, sex,

AST, and ALT), foowed by combination of these variabes into

a joint transaminase and heath history mode (mode 3: age, sex,

pack-year of smoking, acoho drinking, physica activity, diabetes,

AST, and ALT). Finay, mode 3 was extended by adding HBV test

resuts and AFP (mode 4: age, sex, pack-year of smoking, aco-

ho drinking, physica activity, diabetes, AST, ALT, AFP, and HBV)

and further extended by adding HBV and HCV test resuts and

AFP (mode 5: age, sex, pack-year of smoking, acoho drinking,

physica activity, diabetes, AST, ALT, AFP, HBV, and HCV) vari-

abes. HBV and HCV variabes were dichotomized as positive or

negative as described above. In seecting the cutoff points for AST

and ALT, we set the reference group at different starting points

(eg,


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each additiona 5 IU/L at a time through mutipe iterations. We

found that the reference at 25 IU/L was best to differentiate risk

groups (ie, there was a substantia increase in HRs when exceeding

25 IU/L in a scenarios). It is to be noted that the 25 IU/L cutoff

point is much ower than the upper imit of the norma reference

(ULN), usuay set around 40 IU/L (17).

Mode goodness of fit was assessed in terms of discriminatory

accuracy and caibration in an interna vaidation. Discriminatory

accuracy for predicting the deveopment of hepatoceuar carci-noma within 10 years was assessed by constructing time-dependent

receiver operating characteristic curves for censored surviva data

(18) and cacuating the area under the curve (AUC). The adequacy

of each fitted mode was aso evauated by cacuating the concord-

ance index (C-index), which aso measures the mode discrimina-

tory accuracy, using the training and vaidation sets. Simiar to

AUC, we cacuated C-index based on a 10-year prediction. A tota

of four datasets were used in cacuating the C-index: one training

set and one vaidation set for those with an HCV test, and one

training set and one vaidation set for those without an HCV test.

We assessed interna caibration of the modes by determining the

extent of agreement between predicted and observed events in10 years (ie, caibration) (19) and then created a cross-vaidated ca-

ibration pot. We used the whoe study popuation to perform the

10-fod cross-vaidated caibration for different modes, in which

the study popuation was randomy divided into 10 equa sub-

sets with nine subsets as training set and one subset as testing set.

Cross-vaidated predicted probabiity was cacuated in each decie.

The 5- and 10-year absoute risks were cacuated from baseine

probabiity and reative risk profie from the Cox proportiona haz-

ard regression mode, using the standard equation for surviva data

with censored observations (20):

F t S tb X b M j j j jj

p

j

p

( ) ( )exp

= [ ] ( )( )==1 110

whereF(t) denotes the probabiity of deveoping cancer in tyears;

S0(t) is the baseine surviva function; bjis the regression coefficient

for thejth variabe (Xj);Mj denotes the mean eve ofXj;p is the

number of variabes.

We derived risk scores for each statisticay significant predictor

based on regression coefficients in the Cox proportiona hazards

regression mode foowing the reported procedures (21). In a

modes, reference eve of a particuar risk factor received a risk

score of zero. For a particuar risk factor, risk score was assigned

as integer points to each risk eve and cacuated as a weighted

distance from each eve to the reference eve of that particuar

risk factor.A statistica tests were two-sided, and a P-vaues ess than

.05 were considered statisticay significant. Statistica anayses and

modeing were performed using Stata 10.0 (StataCorp, Coege

Station, TX) and SAS 9.2 (SAS Institute Inc, Cary, NC).

rsus

Risk of Hepatocellular Carcinoma in the Subcohort

Without HCV Test

Among 298 051 subjects in the subcohort without HCV test, 1252

incident hepatoceuar carcinoma occurred. We used data from

this subcohort to deveop four risk prediction modes. In a mod-

es, mae sex and oder age (4059 and 60 years) were statisticay

significanty associated with increased risk of hepatoceuar carci-

noma (Tabe 1). We presented risk factors besides sex and age in

the subsequent text.

Risk predictors that were statisticay significanty associated

with increased or decreased risks of hepatoceuar carcinoma in

mutivariabe modes are shown inTabe 1. In mode 1 (heath his-

tory ony), statisticay significanty increased risks were associatedwith smoking (19.9 vs 0 pack-years, HR = 1.35, 95% CI = 1.13 to

1.61; 10 vs 0 pack-years, HR = 1.32, 95% CI = 1.13 to 1.55), regu-

ar acoho drinking (reguar [consumed 2 drinks/day on 3 days/

week] vs none or occasiona [consumed


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HR = 43.0, 95% CI = 27.8 to 66.60). In mode 3, a above vari-

abes were associated with a statisticay significanty increased risk,

except for ALT, and physica activity was not associated with a sta-

tisticay significanty decreased risk. In mode 4, HBV+ status was

associated with a statisticay significanty increased risk (HBV+ vs

HBV, HR = 4.04, 95% CI = 3.24 to 5.04). Again, increasing AFP

eve at or above 2.5 ng/mL was associated with statisticay sig-

nificanty increasing risk (2.54.9 vs


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0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

A

Sensitivity

Model 2: AUC=0.900

Model 3: AUC=0.900

Model 4: AUC=0.918

Model 1: AUC=0.807

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

B

Sensitivity

Model 2: AUC=0.912

Model 3: AUC=0.913

Model 4: AUC=0.927

Model 1: AUC=0.793

Model 5: AUC=0.933

Figure 1. Discriminatory accuracy of the models. Discriminatory accuracy for predicting the development of hepatocellular carcinoma within10 years was assessed by constructing receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). A) Subcohortwithout hepatitis C virus (HCV) test. Four models were developed in this subcohort: model 1 was based on health history; model 2 was based ontransaminase only; model 3 was based on health history and transaminase; and model 4 was based on health history, transaminase level, alfa-fetoprotein level, and hepatitis B virus (HBV) status. B) Subcohort with HCV test. A fifth model was added to include the HCV status.


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Figure 2. Internal calibration of the risk prediction models. Calibrationdetermined the extent of agreement between predicted and observedevents in 10 years, and then a cross-validated calibration plot was gen-erated for the different models. The dashed line indicates the referenceline for an ideal model. Solid circles mark the apparent predictions

for each decile, and the cross-validated predictions for each decile aremarked by a cross symbol. Vertical bars indicate 95% confidence inter-vals around the apparent prediction. Model 1 was based on healthhistory only; model 2 was based on transaminase only; model 3 wasbased on health history and transaminase; model 4 was based on


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of AST (2539 IU/L, score = 5; 4059 IU/L, score = 9; 60 IU/L,

score = 13) (Suppementary Tabe 1, avaiabe onine).

The probabiity of deveoping hepatoceuar carcinoma (ie,

absoute risk) in 5 and 10 years, as a function of increasing risk score,

in a modes in the two subcohorts is shown in Suppementary

Figure 2 (avaiabe onine).

Application of Risk Score and Prediction Power of

Transaminases

We appied the modes to predict probabiity of deveoping hepato-ceuar carcinoma in 5 years and 10 years using eight hypothetica

exampes (Tabe 4). These exampes are representative of individuas

from genera popuation with a range of risk profies. For exampe,

a 60-year-od mae with abnorma transaminases, AST (60 IU/L),

and ALT (30 IU/L), without considering any other risk factors, as

in Exampe 1, woud have a hepatoceuar carcinoma risk of 7.3%

(95% CI = 6.5% to 8.5%) and 15.5% (95% CI = 13.6% to 17.3%)

in 5 and 10 years, respectivey, according to mode 2. The same indi-

vidua in Exampe 2, when positive for HBV, woud have a 21.4%

(95% CI = 17.5% to 23.8%) and 38.2% (95% CI = 34.1% to 42.0%)

risk of cancer deveopment in 5 and 10 years, respectivey, according

to mode 4. When this individua is aso positive for HCV, as in

Exampe 3, his risk woud increase to 77.0% (95% CI = 69.1% to

82.8%) and 97.1% (95% CI = 94.7% to 98.4%) in 5 years and

10 years, respectivey, according to mode 5. This atter high risk

coud be substantiay attenuated to 44.6% (95% CI = 37.7% to

50.8%) and 75.8% (95% CI = 69.4% to 80.9%), respectivey, as in

Exampe 4, if ifestye risks were modified by smoking and drinking

cessation and by engaging in physica activity and improving dia-

betes management. In Exampes 5 and 6, we compared an individua

who was HBV+ with norma transaminase eves to someone whowas HBV with abnorma transaminase eves. The HBV individua

with abnorma transaminase eves had a much higher hepatoceu-

ar carcinoma risk compared with the individua with HBV posi-

tivity aone (5-year absoute risk = 5.1% [95% CI = 4.1% to 6.1%]

vs 0.1% [95% CI = 0.1% to 0.2%]; 10-year absoute risk = 11.8%

[95% CI = 9.9% to 13.6%] vs 0.3% [95% CI = 0.2% to 0.3%]).

Exampe 7 shows the substantia benefit of ifestye modification for

the high-risk individua in Exampe 6, which woud reduce his can-

cer risk by more than 50%. Exampe 8, an individua with HCV+

status, woud have a simiar risk to the individua in Exampe 5 with

HBV+, as ong as transaminases were norma.

health history, transaminase, alfa-fetoprotein level, and hepatitis Bvirus status; and in model 5, hepatitis C virus (HCV) status was added

to model 4. A) Subcohort without HCV test. B) Subcohort with HCVtest.

Figure 2. (Continued)
http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djs372/-/DC1http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djs372/-/DC1http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djs372/-/DC1http://-/?-http://-/?-http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djs372/-/DC1http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djs372/-/DC1http://jnci.oxfordjournals.org/lookup/suppl/doi:10.1093/jnci/djs372/-/DC1


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Table4.

Applicationofthepredictionmodelstohypotheticalindividualswithdifferentriskprofiles*

Riskfactors

Example

1

Example2

Example

3

Example4

Example

5

Example6

Examp

le7

Example8

Transamin

ase

only

HBV+

HBV+andH

CV+

Lifestyleimproved,

HBV+,andHCV+

Normal

transamin

ase

butHBV

+

Abnormal

transaminase

butHBV

Lifestyleim

proved,

abnormaltransaminase

butHBV

Normal

transaminase

butHCV+

Age,y

60

60

60

60

60

60

60

60

Sex

Male

Male

Male

Male

Male

Male

Male

Male

AST,IU/L

60

60

60

60

20

60

60

20

ALT,

IU/L

30

30

30

30

20

30

30

20

Smoking,pack-years

10

10

0

10

10

0

10

Regulardrinking

Yes

Yes

No

Yes

Yes

No

Yes

Physicallyactive

No

No

Yes

No

No

Yes

No

Diabetes

Yes

Yes

No

Yes

Yes

No

Yes

AFP,ng/mL

7

10

10

2

10

10

2

HBVstatus

+

+

+

+

HCVstatus

+

+

+

Predictedprobabilityin

5y(95%CI),

%

7.3(6.5

to

8.5

)

21.4(17.5to23.8

)

77.0(69.1

to

82.8

)

44.6

(37.7to50.8

)

0.1

(0.1

to

0.2

)

5.1

(4.1

to6.1

)

2.1

(1.7t

o4.1

)

0.1

(0.1

to0.1

)

Predictedprobabilityin

10y(95%CI),

%

15.5

(13.6to17.3

)

38.2

(34.1

to42.0

)

97.1(94.7

to

98.4

)

75.8

(69.4

to80.9

)

0.3

(0.2

to

0.3

)

11.8

(9.9

to13.6

)

4.9

(2.5

to5.7

)

0.2

(0.2

to0.3

)

*

Themodelswereappliedtocalculatepro

babilityofdevelopinghepatocellularcarcinomaineighthypotheticalindividualswithdifferentriskprofiles(ie,

differentAST,ALTlevels,

lifestylefactorssuchassmoking,

drinking,physicalactivity,andhepatitisB

andCstatus)whorepresentedtheriskprofiles

inthegeneralpopulation.

Lifestyleimprovedm

eansquitsmoking,stopdrinking,orbecomingp

hysicallyactive.

Predicted

probabilitywascalculatedfromabsoluterisk.

ASTorALTlevelsgreaterthan25IU/Lwere

consideredabnormal.

AST=serumaspartatetransaminase;ALT=serumalaninetransaminase

;AFP=alfa-fetoprotein;

HBV=hepatitisBvirus;HCV=hepatitisCvirus;CI=confidenceinterval;=notapplicable.

ThisindividualonlyhadASTandALTmea

sured.

NodataonhealthhistoryvariablesorHB

VorHCVstatus.Thevaluesofthesevariablesw

ereleftblank.


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We aso anayzed the distribution of hepatoceuar carcinomaincidence by transaminase eves and by HBV and HCV status

(Tabe 5). Among 130 543 subjects in the subcohort with HCV

testing, 109 029 (83.5%) were negative for both HCV and HBV.

Of the 416 subjects who deveoped hepatoceuar cancer during an

average foow-up of 8.5 years, 66 (15.7%) were negative for both

HCV and HBV. Subjects with AST and ALT eves of 25 IU/L or

ower constituted 61.1% (79 762 of 130 543) of the HCV-tested

subcohort, and 7% (29 of 416) of subjects who experienced inci-

dent cancers had AST and ALT eves of 25 IU/L or ower. Incident

hepatoceuar cancers were detected in 4.6% (19 of 416) individu-

as with both HBV+ and HCV+ status. For those positive for HBV

or HCV, ony 37.2% (48 562 of 130 543) and 33.4% (43 601 of130 543) subjects, respectivey, were aware of their carrier status

(Tabe 5).

Dsusson

To date, pubished prediction toos are ony avaiabe for high-risk

chronic HBV carriers. In this study, we deveoped prediction mod-

es for hepatoceuar carcinoma based on data routiney coected

in a typica office visit with the goa to provide a simpe, efficient,

and widey avaiabe too to identify and quantify cancer risk in the

average-risk popuation. Our resuts showed that the mode with

transaminase aone was best abe to predict hepatoceuar carci-noma. Because this mode is abe to predict hepatoceuar carci-

noma risk with high prediction accuracy without knowing HBV

or HCV, it has great potentia to be transated into cinica use for

genera pubic.

HBV and HCV are we-known risk factors for hepatoceuar

carcinoma, but in this study, transaminase (AST or ALT) eve of 25

IU/L or higher were found be independent risk factors for hepato-

ceuar carcinoma with a inear doseresponse trend. The stepwise

prediction modes, invoving testing for AST or ALT initiay, are

simpe for cinicians to impement in their daiy practice. When

routiney coected AST exceeded 25 IU/L, the risk of hepatoce-

uar carcinoma increased exponentiay with increasing concentra-tions of AST. Because of the increased risks associated with AST or

ALT concentrations of 25 IU/L or higher, such a finding shoud

have triggered further testing for HBV, HCV, or AFP, to yied a

more compete picture. The mode using transaminases aone had

a high prediction power, with an AUC vaue of 0.912, which was

statisticay significanty better than those testing for HBV, HCV,

or AFP aone (see Suppementary Figure 1, avaiabe onine). On

the other hand, subjects with AST or ALT concentrations ess than

25 IU/L, considered norma in this study, can be spared from fur-

ther testing. Athough this norma group contributed 7% of a

hepatoceuar carcinoma incidences in this cohort, they occurred

Table 5. Distribution of study subjects, their awareness, and hepatocellular carcinoma incidence by AST or ALT values and by HBV andHCV status*

Transaminase level

Subjects, No. (%)

HCV and HCV and HCV+ and HCV+ and

Total HBV HBV+ HBV HBV+

All subjects in the HCV subcohort 130 543 (100) 109 029 (83.5) 18 155 (13.9) 3054 (2.3) 305 (0.2)

AST, IU/L


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among neary two-thirds of the genera adut popuation (61%).

The risk eve in this group, with approximatey four incidences per

100 000 person-years, is sufficienty ow and may not be accorded

priority in further testing, because two-thirds of them, if pursued,

woud find their HBV or HCV status negative. At 25 IU/L or

greater, the fase-negative rate for AST was 12.7%. This is sma

compared with the fase-negative rate for HBV (51.7%) or HCV

(59.1%), as the mode shoud not miss those at high risk. Athough

the fase-positive rate for AST (24.2%) was higher than the cor-responding rate for HBV (14.3%) or HCV (2.5%), this difference

is not of much practica importance, because positive AST wi be

foowed-up with HBV or HCV test in this mode.

To our knowedge, this is the first mode that assesses the hepa-

toceuar carcinoma risk of apparenty heathy individuas visiting

their primary care physicians office for a heath checkup. In the it-

erature, prediction toos are ony avaiabe for high-risk individuas,

such as known chronic HBV carriers (79) or chronic HCV car-

riers (10,11). Modes for these high-risk individuas require more

cinica data and more sophisticated data to estimate risks; none

of them address the average- or unknown-risk genera popuation.

A mode for subjects at average or unknown risk is more vauabethan one for subjects at high risk, because detaied cinica data are

readiy avaiabe for the high-risk individuas but much ess avai-

abe for those at average or unknown risk. In this arge cohort, two-

thirds of those with positive HBV or HCV status, both high-risk

groups that avaiabe modes attempt to target, were not aware that

they were carriers (Tabe 5); thus, existing prediction modes were

of itte use for these groups.

The versatiity of the prediction mode we present is high-

ighted by the fact that it is usefu for both the average-risk genera

pubic and high-risk individuas. None of the currenty avaiabe

modes coud assess both HBV and HCV subjects, a arge group

contributing 3040% of iver cancer cases reported in western

popuations (6). In Taiwan, more than 1000 new hepatoceuarcancers coud be estimated to occur annuay in HBV and HCV

individuas (15). This group, constituting 83.5% of our cohort, is

commony overooked cinicay (3), and yet an individua in this

group is estimated to have a 10-year absoute risk of 11.8%, based

on our prediction mode (Exampe 6 inTabe 3). Existing modes,

moreover, were not abe to accuratey assess and may, therefore,

underestimate the risk of cancer in HBV+ and HCV+ patients, a

group contributing neary 5% of a cancer incidences in our study.

Subjects in this group coud have a 10-year risk as high as 97.1%,

as in Exampe 3 estimated by our mode. Thus, the simpe too we

present here has much wider appicabiity than other modes cur-

renty avaiabe. More importanty, this versatiity is accompishedwith high efficiency, by reying on common cinica data that are

often readiy avaiabe. That AST and ALT vaues aone showed

AUC exceeding 0.912 was remarkabe. These tests are inexpensive

and routiney coected in daiy medica practice but have not been

put into use for risk prediction in average-risk settings. Adoption

of this mode can make prediction of hepatoceuar carcinoma

a routine cinica activity among subjects in any category of risk.

Modes 4 and 5 showed the independent effects of AST or ALT on

iver cancer, even in the presence of HBV or HCV, but the effect

was appreciaby attenuated from mode 3. For exampe, the hazard

ratio for AST concentration of 4059 IU/L was 13.93 in mode 3

but reduced to 8.59 in mode 4 when HBV status was known. It was

further reduced to 6.31 in mode 5 when both HBV and HCV sta-

tus were considered (Tabe 2). In other words, HBV or HCV had

an impact on the predictive abiity of the transaminases, and there-

fore they were somewhat associated, but transaminase remained

a major statisticay significant predictor even in the presence of

hepatitis carrier status.

Athough this mode was mainy intended to predict cancer

incidence, we found that it was aso abe to predict mortaity fromiver-reated diseases (hepatoceuar carcinoma and iver cirrhosis)

with simiar precision (AUC = 0.93; data not shown). This find-

ing is an added benefit, and the identica resuts obtained (data not

shown) provided additiona confidence in the vaidity of the mode.

Our prediction mode is vauabe for risk identification and risk

communication. Subjects at high risk of cancer shoud be propery

informed in a timey manner of their reative and absoute risks.

However, the mode has vaue we beyond risk communication

because it is aso abe to estimate the potentia for risk mitigation

associated with heathy ifestye choices. Our mode identified four

statisticay significant risk factors from heath history, namey

smoking, drinking, physica inactivity, and diabetes, with a ofthese having bioogicay reevant and statisticay significant risks,

as previousy reported (13,22,23). Our data show that iver cancer

risk can potentiay be reduced by 3350% through modification

of one or more of these four risk factors. Counseing for ifestye

changes woud add important vaue to the risk prediction process.

These benefits can be seen not ony in high-risk individuas with

positive HBV or HCV status (Exampes 3 and 4 inTabe 3 show a

reduction from 77% to 45% in the 5-year risk) but aso in average-

risk individuas negative for HBV or HCV (Exampes 6 and 7 show

a reduction of simiar size, from 5.1% to 2.1%). Furthermore, in

addition to reducing risk for iver cancer, modifying and eiminat-

ing these ifestye behaviors can have a major impact on a-cause

mortaity (12,13), a fact often negected by cinicians. Reducinga-cause mortaity is ceary as important as reducing iver cancer.

Thus, a further vauabe aspect of this simpe prediction mode is

that it may hep to educate and motivate an individua at high risk

to pursue a wider range of various risk reduction options. These

options range from treating carriers with interferon-ike therapy

(6) to reducing cancer risk by eiminating associated risk factors to

reducing a-cause mortaity.

The high predictive power of eevated transaminase eves

of AST or ALT at 25 IU/L or higher for hepatoceuar cancer

risk, athough not a nove finding, has nevertheess not been fuy

appreciated in the cinic. This finding arose by examining the can-

cer risk at every 5-unit interva across the entire AST and ALTdistribution. The cutoff eve of 25 IU/L is worth remembering,

because it is substantiay ower than the 40 IU/L commony cited

by aboratory standards as the ULN. Neary one-fifth (17.8%) of

the adut popuation in our cohort had an AST between 25 and 39

IU/L, a eve commony dismissed as high norma, and yet their

iver cancer risk was increased by as much as 3.6-fod. Once AST

exceeded 40 IU/L, the risk score given was as arge as, or equiva-

ent to, that of the HBV+ or HCV+ individuas in mode 5. We

aso found that the predictive power of AST and ALT aone for

iver cancer was higher than that of isoated HBV, HCV, or AFP

(Suppementary Figure 1, avaiabe onine). Finay, athough the
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13/13


abnorma ALT has been commony recognized to pay an impor-

tant roe, our data showed that the risk conferred by abnorma

AST was higher than ALT (6.3- to 8.3-fod increase vs 1.9-fod

increase;Tabe 2, mode 5).

There are severa imitations of this study. First, athough our

modes demonstrated an exceent eve of goodness of fit and dis-

criminatory abiity, additiona vaidation in externa popuations

is advised. To some extent, the simiar resuts in the two different

subcohorts, each of which had a arge sampe size, provide inter-na vaidity to our findings, but externa confirmation is needed.

Second, our cohort is drawn from participants engaged in a medi-

ca screening program beonging to an above-average socioeco-

nomic status. This may imit the generaizabiity of our findings.

Nevertheess, the hazard ratios deveoped in this study were inter-

nay standardized and free from seection bias. The sampe sizes of

the two subcohorts were sufficienty arge, representing 3% of the

tota adut popuation in Taiwan. Most resuts derived from these

two subcohorts, such as C-index or reative risks, were simiar.

Third, ony AST or ALT data from the initia visit were used for the

mode, and tempora changes were not considered. Nevertheess,

the predictive power of a singe transaminase test was reinforced inthis study, as in other studies invoving this cohort (12,13).

In summary, the use of transaminase data was best abe to pre-

dict hepatoceuar carcinoma risks, with AUC vaue of 0.90 or

higher. Athough HBV and HCV are we-known risk factors, AST

or ALT concentrations of 25 IU/L or higher had independent and

higher predictive power, even among unknown or HBV or HCV

subjects, and shoud trigger further testing. This simpe too for

the genera pubic more accuratey assesses risk even among groups

previousy thought to be at ow or average risk and may be hepfu

to educate and motivate individuas to pursue options beneficia in

reducing their risk of iver cancer and a-cause mortaity.

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Fundng

This study was supported in part by Taiwan Department of Heath Cinica Tria

and Research Center of Exceence (grant number DOH101-TD-B-111-004 to

CPW), The University of Texas MD Anderson Cancer Center Research Trust

(to XW), and Center for Transationa and Pubic Heath Genomics, Duncan

Famiy Institute for Cancer Prevention and Risk Assessment, The University of

Texas MD Anderson Cancer Center (to XW).

Nos

The funders had no roe, and the authors are responsibe for the study design,

data coection, data anaysis, interpretation, writing of the manuscript, and the

decision to submit the manuscript for pubication.

Affiliations of authors: Institute of Population Science, National Health

Research Institutes,Zhunan, Taiwan (C-PW, YCY, MKT); China Medical

University Hospital, Taichung, Taiwan (C-PW, YCY, MKT, CYH); Departments

of Epidemiology (JL, CE, MH, YY, XW), Gastroenterology, Hepatology and

Nutrition (LM), and Division of Cancer Prevention and Population Sciences

(EH), The University of Texas MD Anderson Cancer Center, Houston, TX; MJ

Health Management Institution, Taipei, Taiwan (CKT).
http://-/?-http://-/?-http://-/?-http://www.iom.edu/viralhepatitishttp://www.uspreventiveservicestaskforce.org/uspstf/uspshepc.htmhttp://www.doh.gov.tw/EN2006/DM/DM2.aspx?now_fod_list_no=9256%26class_no=390%26level_no=2http://www.doh.gov.tw/EN2006/DM/DM2.aspx?now_fod_list_no=9256%26class_no=390%26level_no=2http://www.doh.gov.tw/EN2006/DM/DM2.aspx?now_fod_list_no=9256%26class_no=390%26level_no=2http://www.doh.gov.tw/EN2006/DM/DM2.aspx?now_fod_list_no=9256%26class_no=390%26level_no=2http://www.doh.gov.tw/EN2006/DM/DM2.aspx?now_fod_list_no=9256%26class_no=390%26level_no=2http://www.doh.gov.tw/EN2006/DM/DM2.aspx?now_fod_list_no=9256%26class_no=390%26level_no=2http://www.uspreventiveservicestaskforce.org/uspstf/uspshepc.htmhttp://www.iom.edu/viralhepatitishttp://-/?-http://-/?-http://-/?-

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