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omputer-Aidedetection in the Mammographicetection of Breast Cancer: Where,hy, and How With Screen-Film, Computed

adiography, and Full-Field Digital Mammographyachel F. Brem, MD

Mammography remains the mainstay for screening for breast cancer. However, screen-filmmammography (SFM) is an imperfect examination with 10% to 35% of breast cancers notmammographically visible. A recently developed and implemented approach to the im-proved diagnosis of breast cancer is computer-aided detection (CAD), where computeralgorithms and neural networks are used to mark potential areas of abnormalities on themammogram for the radiologist to evaluate and determine whether additional workup isindicated. CAD has been extensively studied retrospectively and prospectively and hasdemonstrated a 7% to 20% improvement in breast cancer detection. This manuscript willreview the current literature of CAD, the principles of computer assessment of mammo-grams, and where the state of the art is going in this transitional time from SFM to full-fielddigital mammography (FFDM) as well as computed radiography for mammography. CADimplemented with mammography has demonstrated improvements in breast cancer detec-tion for SFM, computed radiography, and FFDM. This manuscript will review the state of theart of CAD for the detection of breast cancer.Semin Breast Dis 9:99-104 © 2006 Elsevier Inc. All rights reserved.

KEYWORDS computer-aided detection, breast cancer, mammography, full-field digital mam-mography, computed radiography

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AD and SFMammography remains the screening examination ofchoice for breast cancer. The use of screening mam-

ography has been shown to result in a mortality reductionf up to 44%.1 However, screen-film mammography (SFM) isn imperfect examination. The sensitivity of mammographyanges from approximately 70% to 90%.2 Thus, for a womanith breast cancer, the probability that her cancer will beetected with mammography is 70% to 90%, and the prob-bility that it will not be detected is 10% to 30%. There needo be methods to improve cancer detection with mammogra-

reast Imaging and Interventional Center, The George Washington Univer-sity, Washington DC.

ddress reprint requests to Rachel F. Brem, MD, The George WashingtonUniversity, 2121 I Street, N.W., Washington, DC 20052. E-mail:

prbrem@mfa.gwu.edu

092-4450/06/$-see front matter © 2006 Elsevier Inc. All rights reserved.oi:10.1053/j.sembd.2007.01.004

hy. The interpretation of mammograms is dependent onany factors, including the experience of the radiologist asell as the diligence of the radiologist.Even if all factors were optimized, there would still be

alse-negative mammograms as a result of the limitations ofuman perception. Imperfect perception is a factor in ouraily lives. It is not uncommon for us to search for our keys ase try to leave our home and be frustrated by the inability tond our keys. Often we do find them on the counter, exactlyhere we began our search. Why did we not “see” the keys

nitially? The answer lies in the complex, highly studied, andncompletely understood field of perception. Even if some-hing is directly in front of us, we might not “see” it. The sameolds true for breast cancers and mammograms. There arehree reasons a cancer may be missed: it may not be visible onhe mammogram due to overlying dense breast tissue, it maye visible on the mammogram but not detected by the inter-

reting radiologist (ie, an oversight), or the radiologist may

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isualize the finding and incorrectly interpret it as benign.linical studies have shown that 30% to 70% of breast can-ers diagnosed at screening mammography are visible in ret-ospect on prior examinations and that detection errors areesponsible for approximately half of missed breast cancers,ith interpretation errors accounting for the other half.3 One

pproach to increasing breast cancer perception and mam-ographic accuracy is double-reading; mammographic in-

erpretation by two radiologists improves breast cancer de-ection by 5% to 15%.4,5 However, this approach is noteasible as a standard of care as it requires unavailable addi-ional resources. With the current shortage of breast imag-rs,6 it is not practical to expect two radiologists to interpretammograms.

igure 1 (A) Mammogram with marks pointing out potential areas ofbnormality to the radiologist. (B) Close-up of a spiculated massoval) and calcifications (each denoted by a blue arrowhead). Theeatures are used by the computer algorithms to determine whethero mark the area for the radiologist. (Color version of figure is

vailable online.) D

Computer-aided detection (CAD) has been developed asn effective and clinically important means of improvingreast cancer detection. CAD helps the radiologist detect po-ential areas of concern on mammograms after an initial re-iew of the mammograms by the radiologist. CAD reviewshe mammogram, utilizing digital data, algorithms, and soft-are to alert the radiologist that a potentially significant find-

ng is present on the mammogram, be it a mass (ie, mass,rchitectural distortion, asymmetric density, or speculation)r microcalcifications. The results are then displayed suchhat the region of interest is pointed out to the radiologist byarking the area on the mammogram (Fig. 1). The radiolo-

ist must then determine whether the region identified by theAD is significant, ie, warrants additional workup. It is crit-

cal to note that the proper use of CAD is for the radiologist tovaluate the mammogram first, without the use of CAD andhen to review the areas identified by CAD as a means ofncrementally improving cancer detection.

Retrospective studies evaluating the performance of CADor the detection of breast cancer have shown that CAD canetect cancers which manifest as both masses and/or micro-alcifications with high accuracy, and have demonstratedreater than 20% improvement in breast cancer detectionhen CAD is used.7,8 In all studies, the performance of CAD

or the detection of microcalcifications is better than massesith a sensitivity of approximately 96% for microcalcifica-

ions and 85% to 90% for masses (Table 1).A number of excellent prospective studies of CAD in the

linical setting have been reported (Table 2). Morton andoworkers found a 7.6% improvement in cancer detectiontudying over 12,000 screening examinations.9 In the aca-emic setting, Birdwell and coworkers reporting on 8682

able 1 Sensitivity of Microcalcifications and Masses

Paper

Sensitivity forMicrocalcifications

(%)

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rem (AJR2005 – density)

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arren Burhenne(Radiology 2000)

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able 2 Prospective Studies Analyzing the Impact of CAD onreast Cancer Detection Using SFM

StudyPercent

Improvement in Cancer Detection

reer, et al. 19.5irdwell 7.4upples 16.1orton 7.6

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CAD and detection of breast cancer 101

creening mammograms showed 7.4% more cancers detectedsing CAD.10 Cupples and coworkers showed a 16.1% increase

n cancers detected at screening using CAD11 and Freer andlissey, reporting on more than 12,000 prospectively evaluated

creening mammograms, showed an increase of 19% in canceretection using CAD in a community practice.12

CAD also has been shown to be of clinical benefit in theiagnostic population. In all of these studies, the additionalcreen-detected cancers were stage 0 or 1. Dean and Ilventoemonstrated a 10.8% improvement in cancer detection us-

ng CAD in both screening and diagnostic mammogramsith a 13.3% in the screening population.13 Furthermore,utler and coworkers reported that 7.6% of cancers diag-osed in women with palpable masses or other focal symp-oms were located away from the clinical abnormality, andAD correctly marked 87% of those cancers.14 This studyemonstrates that, in the diagnostic population, CAD can

mprove cancer detection in regions other than those with thelinical symptoms.

The role of reader experience on the impact of CAD onancer detection has been studied. The studies describedbove demonstrating incremental cancer detection in thelinical setting were all done by experienced mammogra-hers. It has been demonstrated that the incremental gain inancer detection is greater in less experienced mammogra-hers,15 and therefore, it is reasonable to assume that the gain

n cancer detection reported is lower than would be found ingeneral radiology practice where the physicians may not beedicated breast imagers.The question as to whether CAD is effective in detecting

reast cancer in different breast densities, different size can-ers, and different histopathologic types of cancer has beenaised. The sensitivity of mammography for the detection ofreast cancer decreases from 85% to 65% in women withense breasts.4 Therefore, CAD would be even more helpfulo radiologists if it could detect cancer in women with densereasts. Studies have demonstrated that CAD is equally effec-ive in detecting cancers in women with dense and nondensereasts,16,17 and presumably its utility in women with dense,ore challenging mammograms may be greater. Further-ore, the size of the cancer does not impact the ability ofAD to detect the lesion.18,19 In fact, the detection of breastancers 0.5 cm and smaller was equal to the detection ofancers greater than 1 cm. Finally, CAD is not impacted byhe histopathology type of breast cancer. Certain types ofreast cancer are more difficult to identify both mammo-raphically and with clinical examination, such as invasiveobular carcinoma.4 However, several studies have demon-trated that CAD detects invasive lobular carcinoma as well asther pathologic types of cancer.20,21 In summary, scientificvidence shows that CAD improves breast cancer detec-ion in women with dense and nondense breasts, in smallnd large cancers, and in all histopathologic types of breastancer.

These studies reviewed above underscore the breadth ofotential impact of CAD in clinical practice. Radiologistsith the assistance of CAD can find more breast cancers at an

arly stage than they can without CAD. CAD can improve F

reast cancer detection in both the academic and privateractice settings. CAD is useful in all types of breast tissueensity, in different sizes of breast cancer, and different his-ological subtypes of breast cancer. Clearly, all these factorshould translate into better breast cancer detection with CADnd a subsequent reduction in mortality.

ecall Rates andosts With CAD and SFM

he additional areas detected by CAD which the radiologistust evaluate can result in increased patient recall rate and

hereby significant increases in the cost of screening mam-ography as well as anxiety to the patient.Several studies have evaluated whether there is an increase

n patient recall with the use of CAD. Gur and coworkersound no increase in the recall rate.22 Dean and Ilvento foundhat the patient recall rate increased from 6.2% before theirmplementing CAD in their practice to 7.8% with CAD dur-ng the period of their study. However, they note that follow-ng the conclusion of their study, the recall rate in their prac-ice decreased to 6.8%. They suggest that perhaps their recallate was higher during the course of the study as they in-reased their recall rate to not be “beaten” by a machine buthen returned, in the nonstudy environment, to essentiallyheir pre-CAD recall rate.14 In their prospective study in therivate practice setting, Freer and Ullsey found a similar in-rease in recall rate from 6.5% to 7.7%.13 When used cor-ectly, CAD is expected to result in an increase in the recallate to evaluate those findings that were detected with the aid ofAD. These modest increases in patient recall with CAD seem toe a small price to pay for the improved detection of breastancer. Future developments of CAD should allow for assess-ent of the likelihood of malignancy of the CAD marks and

hereby result in a reduction in the higher patient recall rate.Areas marked by the CAD system which are not associated

ith a cancer are referred to as false-positive (FP) marks.umerous FP marks can be distracting to the radiologist and

an result in additional time of interpretation. The algorithmssed in CAD must therefore balance high sensitivity with anppropriate and clinically manageable number of FP marks.ince the introduction of CAD technology, the FP rates haveramatically decreased. Currently, the FP rate is approxi-ately 0.4 to 0.6 marks per image or 2 to 2.5 FP marks per

our-view mammogram. This number of FP marks is mini-ally distracting and is manageable. It is possible that furtherevelopments in CAD may result in further reduction in theP rate (Table 3).

able 3 FP Marks per Image on Film Screen Mammography

Study FPs per Image FPs per Case

ahoney29 0.5 2.4offmeister30 0.6 2.4rem31 0.4–0.7 1.6–2.9astellino32 0.5 2.0

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rocess ofAD for SFM and FFDM

he algorithms used in CAD include image processing, fea-ure computations, and pattern recognition technology toetect mammographic features indicative of malignancies.uch features include the size, opaqueness, circularity,rightness, compactness, intensity, texture, area, location,nd orientation. The initial step involves segmentation of themage whereby areas of the image with similar characteristicsre grouped together. An example of this would be the skinine which differentiates the breast from the nonbreast re-ions on the mammogram. In this way, only the breast pa-enchyma will be analyzed by the computer. It will also seg-ent out the boundaries of masses and apply imagerocessing to determine whether the mass has suspiciousharacteristics. The algorithms are obtained from “teaching”he computer by evaluating and determining suspiciousharacteristics of malignant masses. The assessment of theignificant features of the mass (Fig. 2) or of microcalcifica-ions is critical to optimal performance of the CAD system. Aarge number of features are used to analyze the lesion. Theystem must then determine whether a threshold has beeneached by the suspicious characteristics of the lesion to war-ant marking it for the radiologist’s review.

The computer analysis must be performed on digitized datand therefore the entire process of analyzing the mammogramequires digitization of the mammogram. With SFM, part of theAD system is the high-resolution digitizer that converts analog

nformation into digital information. In this process, the mam-

igure 2 Types of characteristics utilized by the algorithm in deter-ining a lesion’s characteristics which is then used by the computer

fo “decide” whether to mark the region.

ogram must be fed into the digitizer (Fig. 3) and the data arehen transferred to the computer for analysis. Although the digi-ization process time has been significantly reduced by techno-ogical improvements, it still requires about 23 seconds a filmverbal communication, iCAD, Inc., Nashua, NH). AlthoughAD is easily integrated into the workplace for the screeningopulation, it is more difficult to integrate into the workflow forhe diagnostic mammograms as the real-time digitization ofammograms can be challenging.The increasing implementation of FFDM in the United

tates23 in which digital data are acquired results in the ability tonalyze the mammogram without the analog-to-digital conver-ion step. This is a significant improvement in terms of work-ow, and CAD results are essentially instantaneously availableor interpretation. Although there are conceptual similarities be-ween the application of CAD in SFM and FFDM, there are somemportant differences. Screen-film mammograms must be digi-ized before the CAD algorithms are applied, whereas digitalammograms are captured directly, and might be more accu-

ate with subsequent improved performance and improved can-er detection. The recent comprehensive Digital Mammo-raphic Imaging Screening Trial (DMIST) evaluating FFDM andomputed radiography (CR) demonstrated equivalency in can-er detection by a radiologist with SFM and FFDM.24 In threeubpopulations, cancer detection was significantly better withFDM, that is, in women with dense breasts, in pre- and peri-enopausal women, and in women under age 50. Nevertheless,

here were still cancers which were detected on SFM which wereot detected with FFDM. Although FFDM is a newer, moreersatile and possibly better technology for the detection ofreast cancer, limitations in search performance are not ad-ressed nor overcome by the development and use of FFDM.ith the compelling data demonstrating the efficacy of CAD for

he improved and earlier detection of breast cancer coupled withhe operational workflow efficiency of CAD and FFDM, CADill likely be used with FFDM in the future. In fact, over 80% ofFDM units currently being purchased are equipped with CADverbal communication, General Electric, Milwaukee, WI andologic, Inc., Bedford, MA).

AD and FFDMtudies have demonstrated that the performance of CADith FFDM is equivalent to that with SFM. O’Shaughnessey,astallino, and coworkers reported their findings evaluatingAD (ImageChecker, R2 Technology V2.3, Los Altos, CA)ith FFDM in 90 biopsy proven cancers and comparing CADerformance with 1083 screen-detected cancers with SFM.25

he CAD system with FFDM correctly marked 97% (33/34)f microcalcification lesions and 84% (47/56) of mass le-ions, for an overall sensitivity of 89% (80/90). The averageumber of false marks per image on the normal cases was.55 (157/284). CAD performance with SFM was 90% over-ll with 98% (399/406, P � 0.5) for microcalcifications, 86%580/677, P � 0.7) for masses, and 90% (979/1083, P �.6). There were 0.5 false marks per image on the normalases. This study demonstrates the equivalency of CAD per-

ormance with FFDM and FSM.

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CAD and detection of breast cancer 103

In a second study, Baum and coworkers26 found similaresults. Sixty-three cases of histologically proven breast canceretected with FFDM (Senographe 2000D, GE Medical Systems,ilwaukee, WI) were analyzed using a CAD system (Imagehecker V2.3, R2 Technology). Fourteen of these malignanciesere characterized by microcalcifications, 37 by masses, and 12y both. They demonstrated CAD detection rates of 81% forasses and 89% for microcalcifications.Our group evaluated CAD with FFDM in 45 cases of biop-

y-proven cancer (GE Health Care, Senographe® 2000D)nd 899 detected with SFM. The mammographic examina-ions were evaluated by a CAD system (iCAD Second Look®,ersion 7.2, Nashua, NH). The CAD system was specificallyeveloped with CAD algorithms that adapt to the character-

stics of FFDM and SFM, so the system performance wasxpected to be consistent with both mammographic exams.he sensitivity of cancer detection by CAD was assessed withFDM and SFM based on mammographic appearance asasses or calcifications, in which architectural distortions

nd focal asymmetric densities were included with masses.ormal cases with FFDM (n � 38) and SFM (n � 147) weresed to compare the system FP rate. The sensitivity of CAD

Figure 3 (A) Analog (film-screen) mammogram is digitizwith the resultant output demonstrating the mammoradiologist to evaluate. (B) A FFDM on the monitor withno time needed for the analysis and the CAD results occuthe differences in film-screen versus FFDM in integratio

ith FFDM was 89%, with CAD detecting 40 of 45 cancers, s

nd CAD sensitivity with SFM was 90%, where CAD detected09 of 899 cancers. Therefore, the detection rate of CAD withFDM and SFM was the same (P � 0.81). CAD with FFDMetected 100% (8/8) of cancers appearing mammographi-ally as calcifications and 86% (32/37) as masses. CAD cor-ectly marked 88% (29/33) of masses that were spiculated.he CAD system FP rate was 0.4 marks per image with FFDMnd 0.5 marks per image with SFM.27

It is often overlooked that in the DMIST trial nearly one-hird of cases were CR (Fuji Mammography, Stamford, CT).herefore, it is important to evaluate the performance of CADn the newest modality for mammography (FDA approvedn 2006). Our group did exactly that. We evaluated theerformance of a CAD system with CR. Fifty-three cases ofreast cancer from clinical trials conducted at 2 sites withR mammography (Fuji CR for Mammography) were eval-ated by a CAD system (iCAD Second Look®, version.2). Overall, 47 (89%) of 53 cancer cases were detectedy the CAD system. We evaluated CAD sensitivity forancers by pathologic cancer size and found that in can-ers 1 to 10 mm (n � 18), sensitivity was 83%; 11 to 20m (n � 17), sensitivity was 88%; 21 to 30 mm (n � 12),

e digital information is then analyzed by the computers well as any regions of suspicious findings for theD marks noted directly on the image. There is virtuallytially “real time.” (C) Schematic diagram demonstratingAD results. (Color version of figure is available online.)

ed. Thgram athe CAr essen

ensitivity was 92%; and greater than 30 mm (n � 6),

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ensitivity was 100%. The CAD system FP rate was 0.55er image.28

onclusionsFDM and CR mammography is a newer technology thanFM and, therefore, there are fewer studies evaluating theechnology. Nevertheless, there is little doubt that CAD withFDM is an important tool that should allow for the im-roved detection of breast cancer. There is clearly a need forrospective studies evaluating CAD with FFDM and CR mam-ography in the clinical setting. However, it is also clear thatAD is an important, clinically available tool for the improvedetection of breast cancer. Many missed cancers are not visual-

zed with SFM due to oversight and human misperception.FDM will likely not significantly impact that. Therefore, withhe compelling data that CAD improves breast cancer detections well as the repeatedly verified findings that CAD improvesreast cancer detection in women with fatty as well as densereasts, in large and small cancers, and in breast cancers regard-

ess of their pathology, it is critical that we utilize CAD withFDM, even if the prospective study results are not yet com-lete. The performance data for FFDM and CR are now availablend support equivalency with SFM. If we do not implementFDM and CR mammography with CAD, we will be denyingatients the opportunity to have earlier and improved detectionf breast cancer. Prospective and preferably comparative studiesf both CAD performance with SFM as well as with FFDM willetter define these ongoing questions.

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