is Medical Director and
is a Breast Imager at the Susan G. Komen Breast Center, Peoria,
Breast cancer is among the most common forms of cancer in
American women. Second only to skin cancer, it accounts for one of
every three cancer diagnoses. According to data from the American
Cancer Society, the year 2001 witnessed approximately 192,200 new
cases of invasive breast cancer, nearly 47,100 additional cases of
in situ breast cancer, and some 40,200 breast cancer deaths.
Only lung cancer accounts for more cancer deaths in women.
Early detection is critical to reducing breast cancer mortality
and improving patient outcomes. Screening mammography is the best
method available for detecting breast cancer early, when treatment
is most effective. With a reported sensitivity of 70% to 90%,
mammography is highly effective. It is, nonetheless, imperfect.
Intensive research has focused on ways to improve detection of
subtle mammographic abnormalities that may signal the earliest
stages of tumor development, but may be overlooked during visual
Evaluation of mammograms by more than one radiologist--so-called
double reading--has been shown to improve sensitivity
substantially, by as much as 15%,
but at considerable time and expense. Computer-aided detection
(CAD) offers an alternative method of double reading that may be
in reducing the rate of false-negative mammographic
Computer-aided detection systems use mathematical algorithms to
analyze mammograms for possible abnormalities, including masses,
microcalcifications, distortions in breast architecture, and
asymmetric densities. After an initial examination of the
mammographic images, the radiologist reviews potentially suspicious
areas highlighted by the CAD software and determines whether they
warrant further work-up. In this way, the CAD system functions as
an automated second reader that confirms the presence of suspicious
areas of breast tissue or identifies possible abnormalities that
might otherwise be missed.
Although commercially available CAD systems operate on similar
principles, there are differences in their design and performance.
How these differences affect their relative performance in the
detection of biopsy-proven breast cancer has been compared for two
CAD systems in a study that used the mammograms that led to the
diagnosis of each case.
The study reported here compared the same two CAD systems,
evaluating their ability to detect breast cancers that had been
missed during conventional mammographic interpretation.
Methods and materials
Consecutive cases of breast cancer were identified from five
private-practice mammography facilities in central Illinois between
April 1998 and June 2000. Patient volume at the mammography centers
covered a wide range, from low to high volume. All facilities and
radiologists were certified under the Mammography Quality Standards
Clinical records from each case were examined to determine
whether the patients had had a negative screening mammogram within
2 years prior to the diagnosis of cancer. A total of 93 such cases
were identified for which a prior mammogram was available.
Two MQSA-certified radiologists at the Susan G. Komen Breast
Center, Peoria, IL, examined the 93 prior mammograms to identify
findings that warranted any type of follow-up, including comparison
with prior films, acquisition of additional mammographic views, and
biopsy. The radiologists, who worked independently of one another,
were not informed about the specific clinical history associated
with the prior mammograms, but were aware that, in each case,
cancer had eventually been detected.
The mammographic films were also processed by two CAD systems:
, version 4.0 (CADx Medical Systems, Quebec, Canada) and
, version 3.1 (R2 Technology, Los Altos, CA). At the time of the
study, version 4.0 of Second Look had just been approved by the
Food and Drug Administration (FDA) for sale in the United States.
Version 3.1 of ImageChecker had not yet been approved by the FDA,
but was the latest version of the software in use in Canada.
One mammographer later reviewed the exams, along with imaging
and clinical information from the time of cancer diagnosis, in
order to identify whether there was evidence of breast cancer on
prior mammography. The CAD output was also examined to identify
cases in which CAD had accurately highlighted the site of
subsequent cancer. The CAD output and the radiologists'
interpretations of prior exams were correlated. These correlations
were used to assess CAD-identified imaging features that would lead
to further patient work-up.
There was retrospective evidence of cancer in 56% (52 of 93) of
the prior mammograms. Table 1 outlines the characteristics of
cancers that could be detected on retrospective review (missed
cancers) and those that could not (true negatives).
Cases were grouped according to the number of radiologists who
indicated a need for further work-up after reviewing each
mammogram. The degree of agreement between the two radiologists was
considered an indication of how a single radiologist might respond
to the identification by CAD of a suspicious mammographic
When abnormalities were identified by both radiologists, the
likelihood was considered to be high that identification of these
same findings by CAD would prompt a single radiologist to recall
the patient for additional follow-up.
Findings identified by only one radiologist signaled a
medium-to-low likelihood that a similar finding on CAD would prompt
recall of the patient for further work-up. Findings identified by
neither radiologist were considered unlikely to prompt patient
recall, regardless of the CAD results.
Overall, Second Look (SL) correctly identified 60% (31 of 52) of
the missed cancers, and ImageChecker (IC) identified 52% (27 of 52)
(Table 2). Of the high-likelihood findings, SL identified 12 of 12
calcifications, 4 of 11 masses, and 1 of 1 distortion. By
comparison, IC identified 9 of 12 calcifications, 8 of 11 masses,
and 1 of 1 distortion. Of the medium-to-low likelihood findings, SL
identified 4 of 4 calcifications, 4 of 8 masses, and 1 of 2
distortions, whereas IC identified 1 of 4 calcifications, 4 of 8
masses, and 0 of 2 distortions.
Particularly in the earliest stages of breast cancer,
abnormalities on screening mammography can be very subtle.
Mammographic interpretation by more than one radiologist has been
shown to improve sensitivity, but at a substantially increased cost
and investment of radiologist time.
Computer-aided detection systems represent an alternative
approach to improving the diagnostic accuracy and consistency of
mammographic interpretation. In essence, the CAD system offers an
automated "second opinion" that the radiologist can consider when
reviewing the mammogram and arriving at a diagnosis.
Recent research suggests that CAD systems offer information that
can influence the interpretation of mammograms in clinically
important ways. A study by Freer and Ulissey
compared the interpretation of nearly 13,000 screening mammograms
by radiologists alone and with the assistance of CAD. They found
that nearly 20% more cancers were detected with the assistance of
CAD and that a larger percentage were early-stage malignancies
(78%, as compared with 73% of those identified by radiologists
A study by Burhenne et al
examined whether the use of CAD could reduce the false-negative
rate in screening mammography. Investigators from 13 mammographic
facilities identified a total of 427 patients whose screening
mammograms had led to the detection of biopsy-proven cancer, and
who had a prior mammogram on file. Blinded retrospective review
showed that mammographic interpretation by the original
radiologists had a false-negative rate of 21%, and that CAD
prompting had the potential to reduce that rate by 77%.
In a related study, Birdwell et al
found that of the 115 cancers missed on screening mammography, 30%
were calcifications, of which 49% were clustered or pleomorphic.
The remaining 70% were mass lesions, of which 40% were spiculated
or irregular. The CAD software marked 86% of the missed
calcifications and 73% of the missed masses.
The study reported here compares the performance of two
different CAD systems for the detection of breast cancers missed by
conventional evaluation of mammographic films. Because commercially
available CAD systems use proprietary mathematical algorithms to
distinguish normal from abnormal breast tissue, it is possible they
differ in their abilities to detect breast cancer.
The study's findings confirm previous evidence that CAD markedly
improves the detection of suspicious breast lesions that warrant
recall of patients for further work-up. Computer-aided detection
was particularly effective in highlighting calcifications, which
are often the earliest signs of breast cancer. The study's findings
also suggest, however, that there may be clinically meaningful
differences in the accuracy with which CAD systems detect both
calcifications and masses.
Overall, the performance of Second Look was superior to that of
ImageChecker. Second Look correctly identified 60% of the missed
cancers, and ImageChecker identified 52%. Second Look was also
better at detecting calcifications in "high-likelihood" cases,
where such information would be most likely to prompt further
work-up of patients. ImageChecker detected more masses in this
high-likelihood group, and the two systems detected the same number
of distortions in breast architecture. In the medium-to-low
likelihood cases, Second Look identified more distortions and
calcifications than ImageChecker. The two CAD systems were equally
effective in highlighting masses.
In the case of high-likelihood lesions, CAD prompting could be
expected to result in the recall of all patients for further
work-up. Only a portion of patients with medium-to-low likelihood
findings would be recalled, however. Thus, of the 52 "missed"
cancers, prompting by Second Look would have contributed to the
recall of 33% (17 of 52) to 50% (26 of 52) of patients for further
work-up. By comparison, ImageChecker would have contributed to the
recall of between 35% (18 of 52) to 44% (23 of 52) of patients.
Computer-aided detection systems improve the accuracy and
consistency of mammographic interpretation. In a side-by-side
comparison of two such systems, Second Look's overall performance
was better than that of ImageChecker. Additional research could
further improve the mathematical algorithms that CAD systems use to
distinguish normal from abnormal breast tissue.