Comparison of percent density from raw and processed full-field digital mammography data.

0.26 MB·English

by  Vachon, Celine M

Vachonetal.BreastCancerResearch2013,15:R1
http://breast-cancer-research.com/content/15/1/R1
RESEARCH ARTICLE Open Access
Comparison of percent density from raw and
processed full-field digital mammography data
Celine M Vachon1, Erin EE Fowler2, Gail Tiffenberg2, Christopher G Scott1, V Shane Pankratz1, Thomas A Sellers2
and John J Heine2*
Abstract
Introduction: Mammographic density has been established as a strong risk factor for breast cancer, primarily using
digitized film mammograms. Full-field digital mammography (FFDM) is replacing film mammography, has different
properties than film, and provides both raw and processed clinical display representation images. We evaluated
and compared FFDM raw and processed breast density measures and their associations with breast cancer.
Methods: A case-control study of 180 cases and 180 controls matched by age, postmenopausal hormone use, and
screening history was conducted. Mammograms were acquired from a General Electric Senographe 2000D FFDM
unit. Percent density (PD) was assessed for each FFDM representation using the operator-assisted Cumulus method.
Reproducibility within image type (n = 80) was assessed using Lin’s concordance correlation coefficient (r ).
c
Correlation of PD between image representations (n = 360) was evaluated using Pearson’s correlation coefficient (r)
on the continuous measures and the weighted kappa statistic ((cid:1)) for quartiles. Conditional logistic regression was
used to estimate odds ratios (ORs) for the PD and breast cancer associations for both image representations with
95% confidence intervals. The area under the receiver operating characteristic curve (AUC) was used to assess the
discriminatory accuracy.
Results: Percent density from the two representations provided similar intra-reader reproducibility (r = 0.92 for raw
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and r = 0.87 for processed images) and was correlated (r = 0.82 and (cid:1) = 0.64). When controlling for body mass
c
index, the associations of quartiles of PD with breast cancer and discriminatory accuracy were similar for the raw
(OR: 1.0 (ref.), 2.6 (1.2 to 5.4), 3.1 (1.4 to 6.8), 4.7 (2.1 to 10.6); AUC = 0.63) and processed representations (OR: 1.0
(ref.), 2.2 (1.1 to 4.1), 2.2 (1.1 to 4.4), 3.1 (1.5 to 6.6); AUC = 0.64).
Conclusions: Percent density measured with an operator-assisted method from raw and processed FFDM images
is reproducible and correlated. Both percent density measures provide similar associations with breast cancer.
Introduction isimportanttoevaluatetheperformance ofdensity esti-
Increasedmammographicbreastdensityisanestablished matedonfull-fielddigitalmammography(FFDM).
breastcancerriskfactor[1-3].Irrespectiveofthemethod FilmmammographyandFFDMhavesimilardiagnostic
of measurement, the majority of studies have found a accuracy inbreastcancerscreening[5].However,images
three- to sixfold increased risk of breast cancer in the acquiredfromthesetwoformsofmammographymaynot
highestvs. lowest densitycategories[2].Themajority of appearsimilarondisplay[6].FFDMsystemscanproduce
thesestudiesestimateddensityfromdigitizedfilmmam- images in both the raw and clinical display representa-
mograms.Currently,more than85percentof the Mam- tions, and these display formats differ across manufac-
mography Quality Standards Act certified facilities turers. Clinical display images are processed (that is,
operateFFDMunits[4].Ifbreastdensityistobeusedin enhanced) with algorithms developed by the respective
theclinicforriskassessmentandpatientmanagement,it manufacturersforimproveddiagnosticcapability.Dueto
thesizeofimagesandstorageconsiderations,therawdata
*Correspondence:[email protected] isoftendiscardedleavingonlythisprocesseddataavailable
2H.LeeMoffittCancerCenter&ResearchInstitute,DivisionofPopulation forexamination. Display variationsmay influence opera-
Sciences,12902MagnoliaDrive,Tampa,FL33612,USA
tor-assistedmeasurementsofbreastdensity.Currently,it
Fulllistofauthorinformationisavailableattheendofthearticle
©2013Vachonetal.;licenseeBioMedCentralLtd.ThisisanopenaccessarticledistributedunderthetermsoftheCreativeCommons
AttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,andreproductionin
anymedium,providedtheoriginalworkisproperlycited.
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is not known which FFDM representation is appropriate use and matched with ± 1 year duration of usage), study
forpercentdensityestimation. imagebreastside,andbythethreescreening-historycate-
Inthisreport,weinvestigatedtheintra-measurerepro- gories defined above (that is, referenced relative to their
ducibility and the inter-measure correlation of percent studyimagedate).Forcontrolsmeetingthematchingcri-
densitymeasures(PD)betweenrawandprocessedFFDM teria,themostcurrentmammogram onthe study FFDM
datarepresentations.Wealsoestimatedandcomparedthe unitwasusedasthestudyimage.Studydatawascollected
associationsofPDfromboththerawandprocessedrepre- underaprotocolapprovedbytheUniversityofSouthFlor-
sentationswithbreastcancer. ida, Institutional Review Board (IRB number 104715D),
Tampa, Florida. In accord with this approved protocol
Materials and methods prospectivecasepatientssignedwritteninformedconsent,
Study design and population and retrospective data was collected from patients that
Weusedamatchedcase-controldesigntoexamineasso- signedinformationreleasedocumentspreviously.
ciationsbetween percent density from FFDM and breast
cancer.Thestudypopulationanddatacollectionmethods Acquisition of FFDM images and estimation of percent
were described previously [7]. Briefly, cases were ascer- density
tainedprospectivelyandretrospectivelyfrompatientswith All images were acquired as standard screening, four-
primary unilateral breast cancer attending the breast view mammograms. Craniocaudal (CC) views were used
clinics at the H. Lee Moffitt Cancer Center between as the study images (that is, the non-cancerous CC view
September 2007 and July 2011 (n = 180). Retrospective for cases and the matched side for the controls). All
caseswereselectedfromarecordreview ofpatientswith mammograms were acquired with one General Electric
breast cancer who received a four-view screening mam- (GE) Senographe 2000D FFDM unit (General Electric
mogramonthestudyFFDMunitatH.LeeMoffittCancer Medical Systems, Milwaukee, WI, USA). This system
Centerpriortooratthetimeoftheirbreastcancerdiag- produces data in both the raw and processed image
nosis. Potential prospective case patients were either representations, the latter for clinical display purposes.
Center breast screening patients without mammograms The raw images have 14-bit dynamic range for each
from the study unit or screening patients from the pixel (that is, values raging from 0 to 16383), whereas
surrounding practices visiting the Center for diagnostic the processed images have 12-bit dynamic range (that is,
purposes. All retrospective cases with existing FFDM values ranging from 0 to 4095). The raw image pixel
images on the study unit with primary unilateral breast scale can be considered as the X-ray attenuation repre-
cancerwereselected.Prospectivecaseswererecruitedand sentation, where adipose image regions are bright (large
offeredaregularfour-view(screening)mammogramfrom pixel values) and fibroglandular regions are dark. The
thestudyFFDMunitatthetimeofdiagnosis. processed images have a reversed intensity scale (and
Each case was classified into one of three screening- reduced dynamic range) and appear similar to film
historycategoriesreferencedto thedate oftheirstudyor mammograms. Image data was written to DVD storage
mostcurrentFFDMmammogrampriorto oratthetime in anonymous DICOM format from the GE workstation.
ofdiagnosis, forcontrol matching purposes: women that This data was then uploaded to a UNIX-based server.
had a history of normal screening 1) within 30 months Percent density (PD) measurements were estimated
priorto their studymammogram (n=162),2) outsideof from the raw (non-processed images) and processed
the 30-month window prior to their study mammogram representationFFDMimagesinDICOMformatusingthe
(n = 13), or 3) women attending their initial screening Cumulus3 software (University of Toronto). For a given
mammogram(thatis,noscreeninghistory)(n=5).Inall representation, the dataset consisting of all cases and
categories,themammogramclosesttodiagnosis(including matched control images were de-identified and rando-
amammogramatorimmediatelyafterdiagnosisbutbefore mized. The reader (JH) was blinded to the case-control
treatment) of breastcancerwasascertained forthestudy status and original imageidentifiers. WhenusingCumu-
image. We selected the non-cancerous breast image for lus, the operator sets window levels and thresholds for
examinationtoavoidcancerpresentontheimages,which each image to separate the dense from non-dense tissue
couldskewthedensitymeasure.Controlswithoutahistory and remove the off breast area region from the analysis.
of breast cancer (n = 180) who had archived four-view Percent density was calculated as the total dense area
screeningimagesacquiredwiththestudyFFDMunitwere normalizedbythetotalbreastareatogivethepercentage
retrospectively selected from the pool of women under- ofdensebreasttissueasthePDmeasure.
going screening mammography at H.Lee MoffittCancer
Centerbetween2007and2011.Controlswereindividually Statistical analyses
matched to cases by age (± 2 years), postmenopausal Patient characteristics and density measures were sum-
hormone use andduration(that is,never used or former marized as either the mean and standard deviation, or
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frequency and percentage, as appropriate. For each PD matched design (Table 1). Cases had a higher BMI (26.6
measure, quartiles were defined based on the distribu- vs. 25.3) and were more likely postmenopausal (79% vs.
tion of that density measure among the control subjects 73%) than controls. The mean PD was higher for cases
for all applicable analyses (explicit quartile cutoff values than controls for both FFDM representations (P < 0.01
and ranges are provided in the results below). for the raw data and P < 0.03 for the processed data
Eighty patient study images were selected at random from the paired t test). Mean PD quantities were greater
fromthecontrolstoevaluatereproducibility.PDwasmea- for the processed images than the raw images within
suredbyasingleoperatorattwotimepoints(separatedon cases (P < 0.11) and controls (P < 0.001), although dif-
theorderofweeks)toassessintra-measurePDreproduci- ferences were not significant among the cases (Table 1).
bility within a given data representation. Reproducibility The intra-operator reproducibility for PD was high as
was assessed using Lin’s concordance correlation coeffi- evidentfromtheconcordancecorrelationofr =0.92for
c
cient (r ),whichmeasuresthe strength oftheassociation the raw images, and r = 0.87 for the processed images.
c c
between two measures about a line with an intercept of The regressionanalyses from the repeated PD measure-
zeroandaslopeofone.Wealsoappliedlinearregression ments also showed the degree of reproducibility with m
analysis to evaluate the relationship between repeated = 0.96 ± 0.05, b = 0.76 and r = 0.92 for the raw images
measurements, which was summarized by the slope (m), showninFigure 1,andm =0.81± 0.05,b= 1.3, andr=
intercept(b),andlinearcorrelationcoefficient(r). 0.89 for the processed images shown in Figure 2. The
TheassociationsbetweenthePDvaluesestimatedfrom similarity between the respective r and r indicates that
c
the two data representations were assessed with a linear thefirstandsecondPDreproducibilitymeasurementdis-
regressionandsummarizedwithm,b,andr.Thedistribu- tributionshavecloseagreementinthemeanandvariance
tionquartile concordance betweenthe two PDmeasure- foragivenimagerepresentation.However,thedeviation
mentswasevaluatedwiththeweightedkappastatistic((cid:1)). intheprocessedPDfromtheideal regressionparameter
Thecombinedcase-controldatasetwasusedforboththe values (that is, m = 1 and b = 0) indicates that PD is
regressionand(cid:1)analyses. betterreproducedfromtherawimages.
Conditional logistic regressionwas used to model and The PDmeasurementsfromtheprocessedclinicaldis-
compare the association between quartile measures of play imagesarelinearlyassociated withthePDmeasure-
PD, and breast cancer status, with the lowest quartile ments from the raw images with r = 0.82. As shown in
serving as the reference. Continuous measures (that is, Figure 3,the processed PD is slightly elevatedrelative to
standard deviations) of PD were also investigated with therawPD;theslopeissignificantlydifferentfromunity
conditional logistic regression. The magnitudes of the withm=0.74±0.03,andtheinterceptissignificantlydif-
associationsweresummarizedbyoddsratios(ORs)with ferentfromzerowithb=6.76.Whencategorizingeachof
95% confidence intervals (CIs). The findings are the PD distributions into their respective quartiles (see
presented for these models: (i) un-adjusted (ii) adjusted Table 2 for cutoff values and ranges), the relationship
forbodymassindex(BMI)(kg/m2)only,(iii)adjustedfor between the two measurements shows moderate agree-
bothBMIandbreastarea(orBA,measuredincm2),and ment,withaweighted kappa ((cid:1))of0.64 (95%CI:0.59 to
(iv) adjusted for BMI, BA, and menopausal status (pre- 0.70)[9].Thejointfrequencyquartiledistributionforthe
menopausal if they reported experiencing menstrual two PD measures is provided and compared in Table 2.
cycles, otherwise postmenopausal). In all models, the Of the 360 subjects, 217 (60.3%) were classified into the
ORs for BMI and BA are presented as per distribution samequartilesbyassessmentsmadeontheirrawandpro-
(combined cases and controls) standard deviation cessed images. Ofthe143with discrepant classifications,
increase. SimilarlyforthecontinuousPDmeasures,ORs 131 (91.6%) differed by a single quartile (when applying
are presented as per standard deviation increase for the the published Boyd categories [10], results were similar
respectiveFFDMrepresentation.Inallapplicablemodels, with 66% of the patients classified in the same category
we used premenopausal status as the reference for the and32.5%withinonecategoryshift,seeAdditionalfile1).
menopause binary variable. Additionally as a secondary The PD andbreast cancerassociations forbothFFDM
meansofcomparison,theareaunderthereceiveroperat- representations are summarized in Table 3 (raw) and
ing characteristic curve (AUC) was computed as a sum- Table 4(processed).The ORsandAUCs fortheassocia-
mary of the ability of each model to discriminate tions of breast cancer with categorical (quartiles of
between cases and controls. Methods for comparing PD based on controls, provided in Table 2) and con-
correlatedAUC[8]wereusedinthisevaluation. tinuousmeasures ofPD(perstandarddeviation)arepro-
vided. Because our interest is in the performance of the
Results Cumulus method applied to FFDM image formats, we
Cases and matched controls were of similar age and focusonthesimpleBMI-adjustedmodelsfortheprimary
postmenopausal hormone use, as expected due to the comparison.Theassociationsanddiscriminatoryaccuracy
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Table 1Participant characteristics by case,control, andcombined grouping.
Characteristic Casen Case Controln Control Totaln Total Pd
Mean/SDb Mean/SDb Mean/SDb
or% or% or%
Age 180 58.60/10.46 180 58.53/10.40 360 58.56/10.41 0.232
MenopausalStatus 0.049
Postmenopausal 142 78.89% 132 73.33% 274 76.11%
Premenopausal 38 21.11% 48 26.67% 86 23.89%
PMHa 0.297
Never-used 96 53.33% 101 56.11% 197 54.72%
1-5yrs 30 16.67% 27 15.00% 57 15.83%
6-10yrs 18 10.00% 19 10.56% 37 10.28%
11-15yrs 13 7.22% 9 5.00% 22 6.11%
>15yrs 23 12.78% 24 13.33% 47 13.06%
BMI(kg/m2)c 179 26.56/4.62 180 25.25/4.25 359 25.90/4.48 0.009
Breastarea(cm2) 180 139.01/47.83 180 131.52/40.60 360 135.26/44.46 0.104
PD (%) 180 22.48/14.41 180 19.76/12.89 360 21.12/13.72 0.029
proc
PD (%) 180 21.33/15.66 180 17.70/14.58 360 19.52/15.22 0.009
raw
aPostmenopausalhormonal(PMH)byyears(yrs);bthemeanandstandarddeviation(SD)fortherawandprocessed(proc)percentageofbreastdensitymeasures
(PD),age(years),bodymassindex(BMIkg/m2)andbreastarea(cm2)arealsoprovided;cBMIwasmissingforonecaseobservation;dPvalues(P)were
determinedwiththepairedttest(continuousvariables)ortheexactMcNemar’stest(binaryvariables)bycomparingtherespectivecaseandcontrolquantities.
80
60
)
w
a
R
40
(

2
D
P
20
0
0 20 40 60 80
PD (Raw)
1
Figure1Reproducibilityofpercentdensityfromtherawimagerepresentation.Thisshowsthepercentdensitymeasure(PD)appliedtoa
sampleof80rawimagesattwotimepointslabeledasPD andPD,respectively(diamonds)evaluatedwiththisrelationshipPD =m×PD +b,
1 2 2 1
wheremandbaretheslopeandintercept.Thefittedline(solid)wasestimatedwithregressionanalysisgiving:m=0.96±0.05,b=0.76,and
linearcorrelation=0.92.
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80
60
)
d
e
s
s
e
c
o 40
r
P
(

2
D
P
20
0
0 20 40 60 80
PD (Processed)
1
Figure2Reproducibilityof percentdensityfromtheprocessedimage representation.Thisshowsthepercentdensitymeasure(PD)
appliedtoasampleof80processedimages(samepatientsamplesshowninFigure1)attwotimepointslabeledasPD andPD,respectively
1 2
(diamonds)evaluatedwiththisrelationshipPD =m×PD +b,wheremandbaretheslopeandintercept.Thefittedline(solid)wasestimated
2 1
withregressionanalysisgiving:m=0.81±0.05,b=1.3,andlinearcorrelation=0.89.
ofquartilesofPDfromraw(OR:1.0(ref.),2.6(1.2to5.4), Secondarily, wecomparedthediscriminatoryaccuracy
3.1 (1.4 to 6.8), 4.7 (2.1 to 10.6); AUC = 0.63) and (orAUC)oftheabilityofvariousmodelstoseparatethe
processed images (OR: 1.0 (ref.), 2.2 (1.1 to 4.1), 2.2 (1.1 cases from the controls (Tables 3 and 4). Within either
to 4.4), 3.1 (1.5 to 6.6); AUC = 0.64) with breast cancer representation for both quartile and continuous unad-
were similar. The associations of continuous PD and justed models, the AUC increase due tothe inclusionof
breast cancer for the raw (OR = 1.8 (1.3 to 2.4); AUC = BMI wassignificant(P <0.003), whereas the addition of
0.64) and processed (OR = 1.6 (1.2 to 2.2); AUC = 0.63) the other covariates with BMI individually, or simulta-
images were also similar. In all of the BMI-adjusted neously, produced marginal AUC increases (P > 0.20).
models, the ORs corresponding to the PD and breast However,therewasnostatisticalsignificantdifferencein
cancer associations were significant (Tables 3 and 4). AUC for PD from raw vs. processed images with breast
The BMI variable had the largest influence on the breast cancerforeithertheunadjustedoradjusted models(P>
density associations, as expected [11]. The addition of 0.30).
breast area and menopausal status together strengthened
(marginally) the PD associations for both representations. Discussion
In the fully adjusted continuous PD models, the asso- Severalofourfindingsmeritparticularcommentspecific
ciation of breast cancer with percent density from raw to this FFDM technology. First, PD was reproducible
(OR = 1.8 (1.3 to 2.4)) and processed images (OR = 1.7 whetherassessedfromraworprocessedimagesbutrepro-
(1.2 to 2.3)) showed little difference from the BMI- ducibility was slightly higher for the raw representation.
adjusted models. Second,PDassessedfromthetwoFFDMrepresentations
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80
60
D
P

a
t
a
d
d 40
e
s
s
e
c
o
r
p
20
0
0 20 40 60 80
raw data PD
Figure3Comparisonofpercentdensityfromtherawandprocessedimages.Thisplotshowstherelationbetweenthepercentdensity
measure(PD)appliedtotherawandprocesseddata(diamonds)fortheentirecase-controldatasetevaluatedwiththisrelationshipPD =
processed
m×PD +b,wheremandbaretheslopeandintercept.Thefittedregressionline(solid)showstheprocessedPDasalinearfunctionofthe
raw
rawPDgiving:m=0.74±0.03,b=6.76,andlinearcorrelation=0.82.
Table 2Jointfrequency quartile distribution for the was highly correlated, although PD measured from the
number ofobservations (n) per-quartile for the percent
processed images was slightly greater than from the raw
density measurements (PD) fromthe raw(vertical) and
images. And, earlier analysis showed that PD estimated
processed (horizontal) image representations.
from processed images (same type of FFDM unit used
PD(processed)
here) was less than that estimated from film [12]. These
PD(raw) Quartile1 Quartile2 Quartile3 Quartile4 n findings suggest that when merging mammograms from
[0.0,9.2) [9.2,18.1) [18.1,26.3) [26.3,100) film and FFDM systems for breast density analyses, the
Quartile1 52 16 2† 1† 71 choiceofbothfilmvs.FFDMaswellasFFDMrepresenta-
[0.0,5.5) tion could impact the study. Much of the discrepancy
Quartile2 19 51 21 3† 94 between the two image representations of PD was
[5.5,14.7) accounted for by a one quartile shift. However, a small
Quartile3 3† 28 40 21 92 proportionofsamples,approximately4%(thatis12/360),
[14.7,26.9) showedalargediscrepancyoftwoquartileshifts(Table2).
Quartile4 1† 2† 26 74 103 Further, our inter-FFDM measure agreement is in the
[26.9,100) range of inter-rater PD agreement using various FFDM
n 75 97 89 99 360 models[13].Wedemonstratedhighintra-raterreproduci-
PDquartilerangesandcutoffvalues(correspondingtothoseusedinTables3 bility of boththe raw and processedFFDM PD, whichis
and4)areprovidedunderthequartileheadings.†Observationsthat consistent with findings by other groups using different
experiencedtwointer-representationquartileshifts(thatis,12/360or
types of FFDM units[14]. Finally,thiswork showedthat
approximately4%oftheobservations).
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Table 3Associations ofcategorical andcontinuous percent densityanalysis from rawfull-field digital mammography
(FFDM)representation withbreast cancer(n= 180case andmatched control pairs).
Quartile Case UnadjustedOR BMIadjustedOR BMI,BAadjustedOR BMI,BA,menopauseadjustedOR
PD N=180 (95%CI) (95%CI) (95%CI) (95%CI)
raw
1 26 1.00(Ref.) 1.00(Ref.) 1.00(Ref.) 1.00(Ref.)
2 49 2.21(1.07,4.55) 2.55(1.20,5.42) 2.60(1.21,5.55) 2.83(1.30,6.16)
3 47 2.17(1.07,4.40) 3.12(1.44,6.76) 3.15(1.45,6.84) 3.45(1.55,7.65)
4 58 2.70(1.32,5.49) 4.69(2.08,10.58) 4.85(2.13,11.07) 5.17(2.24,11.96)
BMIa n/a 1.56(1.22,1.99) 1.49(1.13,1.97) 1.46(1.10,1.93)
BAa n/a n/a 1.09(0.83,1.44) 1.11(0.84,1.48)
Menopauseb n/a n/a n/a 2.42(1.00,5.86)
AUCc 0.568 0.631 0.634 0.641
ContinuousPD UnadjustedOR BMIadjustedOR BMI,BAadjustedOR BMI,BA,menopauseadjustedOR
raw
(95%CI) (95%CI) (95%CI) (95%CI)
PDa 1.38(1.07,1.77) 1.76(1.31,2.37) 1.79(1.33,2.43) 1.79(1.32,2.42)
BMIa n/a 1.60(1.25,2.06) 1.52(1.15,2.01) 1.48(1.12,1.97)
BAa n/a n/a 1.12(0.85,1.48) 1.13(0.85,1.50)
Menopauseb n/a n/a n/a 2.06(0.87,4.89)
AUCc 0.571 0.638 0.641 0.655
aTheORsforBMI(kg/m2)andBA(cm2),andPD(continuousmodels)arecitedinperstandarddeviationincrease;bforthebinarymenopausalvariable,pre-
menopausalstatusisthereference;cAUCisameasureofdiscriminatoryaccuracy.AUC,areaunderthereceiveroperatingcharacteristiccurve;BA,breastarea;
BMI,bodymassindex;CI,confidenceinterval;PD,percentdensity;OR,oddsratio.
PD estimated from either representation was associated this system over a wide range of energies [16], which
withbreastcancer. may also influence the raw image display. These differ-
The inter-FFDM representation differences in our ences may also be due to operator preference or natural
findings may be in part due to the pixel dynamic range variation (that is, each image requires its own window
compression (14 to 12 bit) or the two-stage mapping level and threshold adjustments). Because there are rela-
(that is, the mapping applied by the manufacturer) used tively few FFDM reports evaluating PD from FFDM,
to form the processed images from the raw images [15]. the generality of our findings will require further
The detector response is linear in X-ray exposure for investigations.
Table 4Associations ofcategorical andcontinuous percent densityanalysis from processed (proc) full-field digital
mammography (FFDM) representation withbreast cancer(n= 180 caseand matched controlpairs).
Quartile Case UnadjustedOR BMIadjustedOR BMI,BAadjustedOR BMI,BA,menopauseadjusted
PD N=180 (95%CI) (95%CI) (95%CI) OR(95%CI)
proc
1 30 1.00(Ref.) 1.00(Ref.) 1.00(Ref.) 1.00(Ref.)
2 52 1.70(0.93,3.13) 2.16(1.13,4.12) 2.24(1.16,4.30) 2.59(1.32,5.09)
3 44 1.51(0.81,2.81) 2.22(1.11,4.44) 2.35(1.16,4.77) 2.70(1.30,5.62)
4 54 1.90(0.98,3.67) 3.14(1.49,6.60) 3.42(1.59,7.39) 3.99(1.80,8.84)
BMIa n/a 1.50(1.18,1.90) 1.41(1.08,1.84) 1.39(1.06,1.81)
BAa n/a n/a 1.14(0.86,1.51) 1.18(0.89,1.57)
Menopauseb n/a n/a n/a 2.82(1.16,6.84)
AUCc 0.556 0.636 0.638 0.647
ContinuousPD UnadjustedOR BMIadjustedOR BMI,BAadjustedOR BMI,BA,menopauseadjustedOR
proc
(95%CI) (95%CI) (95%CI) (95%CI)
PDa 1.32(1.02,1.69) 1.63(1.22,2.18) 1.67(1.24,2.25) 1.68(1.24,2.28)
BMIa n/a 1.53(1.20,1.94) 1.45(1.10,1.89) 1.42(1.08,1.86)
BAa n/a n/a 1.13(0.85,1.48) 1.14(0.86,1.51)
Menopauseb n/a n/a n/a 2.19(0.93,5.19)
AUCc 0.550 0.628 0.635 0.643
aTheORsforBMI(kg/m2)andBA(cm2),andPD(continuousmodels)arecitedinperstandarddeviationincrease;bforthebinarymenopausalvariable,pre-
menopausalstatusisthereference;cAUCisameasureofdiscriminatoryaccuracy.AUC,areaunderthereceiveroperatingcharacteristiccurve;BA,breastarea;
BMI,bodymassindex;CI,confidenceinterval;PD,percentdensity;OR,oddsratio.
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The present results should be interpreted in light of thepixelscaleisreversedforourrawimages(thatis,lar-
certain limitations and qualifications. Limitations of the ger values corresponded with fatty area). This suggests
study include the relatively limited number of cases and that the raw images from this specific mammography
controls, emphasizing the importance of replicating unit are in the incorrect format and will not display
these findings in larger studies. Also, the use of mam- properly within the Cumulus3interface. To the contrary,
mograms at the time of diagnosis limits our ability to we did not experience any difficulty reading the raw
draw additional conclusions regarding temporality and images without additional preparation. This raw image
risk. And, we were not able to examine or adjust for the pixel scale was automatically inverted (within Cumulus)
perimenopausal category within menopausal status, due so that adipose regions were dark and glandular regions
to the limited nature of the question; however, this bright when reading the raw images into the Cumulus
should not influence the comparison between the two environment. Because we are uncertain as to why the
density measures. These findings apply to one specific raw images displayed properly within the Cumulus3
FFDM design and to a Cumulus operator with consider- interface, in conflict with operating instructions, this
able experience and preferences. Therefore, it will be work should be replicated on both similar and different
necessary to conduct similar research on other FFDM FFDM designs as well. Notwithstanding these artifacts,
technologies with multiple operators and larger datasets the comparisons and findings presented in this report
to ensure our results generalize. Similarly, the operator areinternallyvalid.
was not a clinician, and our results strictly apply in the
research environment and not to the clinical setting per Conclusions
se. Although given an equally skilled operator, we would In summary, PD from the two FFDM representations
expect the findings to theoretically translate to the was reproducible and correlated. Further, the breast
clinic. The detector size for the FFDM design (that is, cancer associations were similar across the data repre-
the earliest FFDM technology) used in our study pre- sentations and agree with those reported previously
cludes its use on women with large breasts with one [2]. However, the raw data representation provided
exposure [6], limiting the range of breast sizes for the slightly better reproducibility. Thus, the raw data may
cases and controls included in the study [7] and conse- be preferable for PD applications when possible.
quently our generalizability to women with large breasts. Although these results are encouraging, additional eva-
Our findings will require replication on newer FFDM luation of prospective and larger study populations,
models that do not have the size limitation. We operators, and system designs is required to confirm
observed slightly higher reproducibility in the analysis of our findings.
the raw data than processed data, but this may reflect
that the operator has more experience labeling raw data Additional material
than processed data rather than any true difference. The
reproducibility analysis was restricted to control images, Additionalfile1:TableS1.Thistableprovidesthejointfrequency
which should not limit generalization because there are distributionforthenumberofobservations(n)per-Boydacategoryfor
thepercentdensitymeasurements(PD)fromtheraw(vertical)and
not large differences in the unadjusted case-control PD
processed(horizontal)imagerepresentations.
distributions. Moreover from our operator’s experience,
the very low-density images, which are less prevalent in
cases, are more difficult to label (that is, in these situa-
Abbreviations
tions the operator may perceive a range of acceptable
AUC:theareaunderthereceiveroperatingcharacteristiccurve;b:intercept;
control settings before the density estimation is per- BA:breastarea;BMI:bodymassindex;CI:confidenceinterval;FFDM:full-field
formed) than high-density images. The reproducibility digitalmammography;κ:weightedkappastatistic;m:slope;OR:oddsratio;
PD:percentdensityorpercentageofbreastdensitymeasure;r:Pearson’s
analysis was performed over a short time period, which
correlationcoefficient;r:Lin’sconcordancecorrelationcoefficient.
c
could result in higher reproducibility compared to a
longer span between evaluations. Finally, we did not Authors’contributions
ThedatabasewasconstructedbyGTandEFunderthesupervisionofJH.JH
have corresponding mammograms from film mammo-
andCVdevelopedthemanuscriptcontent.EF,CS,VP,andJHperformedthe
graphy units to serve as comparison to the raw and pro- dataanalysis.CVandTSprovidedtheepidemiologicguidance.Allauthors
cessed FFDM representations. contributedtomanuscriptcomposition.Allauthorsreadandapprovedthe
finalmanuscript.
TheCumulus software wasdeveloped fordigitizedfilm
data applications, not for FFDM. The Cumulus3 operat- Competinginterests
ing instructions indicate that the input data scale should Theauthorsdeclarethattheyhavenocompetinginterests.
parallel that ofthe display representation images (that is,
Acknowledgements
larger pixel values should correspond with radiographi- ThisworkwassupportedinpartbytheNationalInstitutesofHealthgrants:
cally dense or bright image regions). It should be noted, R01CA114491,R01CA128931,R01CA97396,R01CA140826,andMayoClinic
Vachonetal.BreastCancerResearch2013,15:R1 Page9of9
http://breast-cancer-research.com/content/15/1/R1
BreastCancerSpecializedProgramofResearchExcellence(SPORE)grant
P50CA116201.
Authordetails
1MayoClinicCollegeofMedicine,DepartmentofHealthSciencesResearch,
200FirstStreetSW,Rochester,MN55905,USA.2H.LeeMoffittCancerCenter
&ResearchInstitute,DivisionofPopulationSciences,12902MagnoliaDrive,
Tampa,FL33612,USA.
Received:21May2012 Revised:6November2012
Accepted:18December2012 Published:4January2013
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