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AAPM meeting 2001, Salt Lake City, Utah
CE: Mammography 4:
Update on Computer-Aided Diagnosis in Mammography
Handout*
Maryellen L. Giger, Ph.D.
Professor of Radiology
Department of Radiology
The University of Chicago
MC2026
5841 S. Maryland Ave.
Chicago, Illinois 60637
Phone: 773-702-6778
Fax: 773-702-0371
m-giger@uchicago.edu
* Excerpted and modified from: Giger ML: "Computer-aided diagnosis".
In: AAPM/RSNA Categorical Course in Diagnostic Radiology Physics:
Physical Aspects of Breast Imaging -- Current and Future
Considerations, (Haus A. and Yaffe M., eds.) pp. 249-272, 1999.
I. INTRODUCTION
Computer-aided diagnosis (CAD) can be defined as a diagnosis made by a radiologist
who uses the output from a computerized analysis of radiographic images as a "second opinion"
in detecting lesions and in making diagnostic decisions. The final diagnosis is made by the
radiologist.
Although mammography is currently the best method for the detection of breast cancer,
between 10-30% of women who have breast cancer and undergo mammography have negative
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mammograms (1-6). In approximately two-thirds of these false-negative mammograms, the
radiologist failed to detect the cancer that was evident retrospectively (4,5,7,8). The missed
detections may be due to the subtle nature of the radiographic findings (i.e., low conspicuity of
the lesion), poor image quality, eye fatigue or oversight by the radiologists. In addition, it has
been suggested that double reading (by two radiologists) may increase sensitivity (9-13). Thus,
one aim of CAD is to increase the efficiency and effectiveness of screening procedures by using
a computer system, as a "second reader" (like a “spell checker”), to aid the radiologist by
indicating locations of suspicious abnormalities in mammograms, leaving the final decision
regarding the likelihood of the presence of a cancer and patient management to the radiologist
(14). Since mammography is becoming a high volume x-ray procedure routinely interpreted by
radiologists and since radiologists do not detect all cancers that are visible on images in
retrospect, it is expected that the efficiency and effectiveness of screening could be increased by
CAD. The interpretation of screening mammograms lends itself to CAD since it is a repetitive
task involving mostly normal images.
If a suspicious region is detected by a radiologist, he or she must then visually extract
various radiographic characteristics. Using these features, the radiologist then decides if the
abnormality is likely to be malignant or benign, and what course of action should be
recommended (i.e., return to screening, return for follow-up or return for biopsy). Many patients
are referred for surgical biopsy on the basis of a radiographically detected mass lesion or cluster
of microcalcifications. Although general rules for the differentiation between benign and
malignant breast lesions exist (1,15), considerable variabillity occurs in the interpretation of
lesions by radiologists with current radiographic techniques (6,16). On average, only 10-20% of
masses referred for surgical breast biopsy are actually malignant (1,17-20). Thus, another aim of
CAD is to extract and analyze the characteristics of benign and malignant lesions seen on
mammography in an objective manner in order to aid the radiologist by increasing diagnostic
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accuracy and reducing the numbers of false-positive diagnoses of malignancies; thereby
decreasing patient morbidity as well as the number of surgical biopsies performed and their
associated complications. It is hoped that CAD will improve the reproducibility of
mammographic interpretation. Computerized classification methods are also being extended to
ultrasound and magnetic resonance images of the breast.
The presentations of the various detection and classification methods vary depending on
the details of the specific methodology that are available and on the database employed with
which to evaluate the computerized approach. It is important to note that a comparison of
different computerized methods is not possible due to the use of different databases. That is, it
cannot be assumed that a computerized scheme that achieves a high sensitivity with one database
of mammograms will achieve a similar performance level with another database or with an actual
patient population. In addition to the database, the method of evaluation will influence
performance and expectations of a specific computerized scheme. Some of the computerized
schemes have been tested, or merely demonstrated, only with a few images; whereas others have
used moderate-size databases and statistical testing methods. Performance levels, in the latter
situations, are usually given for the detection schemes in terms of the sensitivity (true-positive
rate) for detection and the number of false-positive detections per image; and for the
classification schemes in terms of the sensitivity for classification and the specificity (i.e., one
minus the false-positive fraction). Such issues will be discussed later.
II. COMPUTER VISION AND ARTIFICIAL INTELLIGENCE
Computer vision involves having a computer extract from a digital image features that
may or may not be otherwise perceived by a human. The development of computer vision
schemes requires a priori information about the medical image (e.g., the mammogram) and
knowledge of various computer processing techniques and decision analysis methods. The
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required a priori knowledge includes the physical imaging properties of the digital image
acquisition system and morphological information concerning the abnormality (e.g., mass lesion
or cluster of microcalcifications) along with its associated anatomic background. That is, a
sufficient database is needed in order to cover the entire range of abnormals and normals.
Computer vision techniques include, in general, image processing, image segmentation and
feature extraction (21,22).
Once the mammographic image is in digital format, the data are accessible for computer
manipulations. Image processing techniques can be employed to enhance features for ease of
extraction later or to de-emphasize unwanted features such as background structures.
Segmentation of an image involves the separation of the image into regions of similar attributes.
Basically, a segmentor only subdivides an image and does not try to recognize the individual
segments. Feature extraction entails the recognition of specific characteristics of the individual
segments.
Once features can be extracted, relevant ones need to be selected and optimally merged
by a classifier. Artificial intelligence techniques include feature selection methods such as stepwise selection and genetic algorithms, and classifiers such as discriminant analysis techniques,
rule-based expert systems, and artificial neural networks. Such techniques are used to merge
computer-extracted features and/or radiologist-extracted features into diagnostic decision aids.
Currently, screen/film combinations are used as the image recording system for
mammographic imaging with investigators in CAD using digitized mammograms almost
exclusively. Developments in full-field digital mammography (FFDM) (29), however, have
approached clinical usage and thus, yielding the opportunity for CAD on FFDM. Digital image
acquisition (film digitization or FFDM) can vary in terms of pixel size and number of
quantization levels. In general, when developing a detection scheme, it is important to consider
the size of the lesion of interest when choosing pixel size. For example, the detection of
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microcalcifications requires a much smaller pixel size (probably around 0.1 mm) than does the
task of detecting mass lesions. In addition, the classification of a lesion in question (to be
discussed later) may require higher spatial resolution (finer digitization) than that needed for
detection. Chan et al. (40,41) have examined the effect of various system parameters, such as
pixel size, quantization and data compression, on the detection performance of computerized
methods. They demonstrated the trade-off between detection accuracy and data compression rate
or digitization resolution.
The availability of high-speed computers and high-resolution film digitizers has been
instrumental in the development of advanced computerized detection schemes. It is assumed that
over 100 research groups in either academia or industry are developing computerized schemes
for the detection of masses and clustered microcalcifications in digital mammograms. Many
involve the use of classifiers to distinguish between actual lesions and false-positive detections,
or between malignant and benign lesions. Examples of these classifiers in CAD schemes include
rule-based methods (48,90), discriminant analysis (69), Baysian methods (68, 85), artificial
neural networks (55,70,71), and fuzzy logic (76). The features for input to these classifiers are
selected using a variety of techniques including step-wise methods (130) and genetic algorithms
(56, 84, 130). Researchers are investigating two types of computer-extracted features:
radiologist-used features (e.g., see 124, 126) and higher-order features that may not be as
intuitive to radiologists (e.g., see 129).
III. COMPUTER-AIDED DETECTION
The computerized detection of lesions involves having the computer locate suspicious
regions, leaving the classification of the lesion totally to the radiologist. It has been reported that
between 30 to 50% of breast carcinomas detected mammographically demonstrate clustered
microcalcifications (23-25), although about 80% of breast carcinomas reveal microcalcifications
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upon microscopic examination (26-29). In addition, studies indicate that 26% of nonpalpable
cancers present mammographically as a mass while 18% present both with a mass and
microcalcifications (25). Thus, most computerized schemes in mammography are being
developed for the detection of mass lesions (77-100) or clustered microcalcifications (42-76,
164).
In order to limit the region of search for lesion detection, the breast region may to be
initially segmented from the image. Bick, et. al (30) and Mendez et al. (31), have described
methods using computer-defined unexposed and direct-exposure image regions to generate a
border around the breast region. Once the breast region is known, preprocessing and search can
be contained to within the breast border to yield potential lesion sites. Some methods incorporate
an equalization of the gray levels near the periphery of the breast in order to correct for the
increased density due to the reduced breast thickness (32,33) Most computer detection methods
can be described by a preprocessing stage during which a threshold is applied on each pixel
based on a feature such as gray level (34), gradient (90), line orientation (35) or a combination of
features (36,37) (e.g., from a feature image). Remaining pixels are then grouped yielding lesion
candidates from which various features can be extracted and merged in an attempt to reduce
false-positive detections.
Various details of developing computer detection methods can be found in proceedings of
the International Workshops on Digital Mammography (101-103).
IV. COMPUTER-AIDED DIAGNOSIS
Once a lesion is detected, a radiologist must make a decision concerning patient
management -- that is, should the patient return to screening, have a follow-up or be sent for
biopsy? This differs from a purely benign versus malignant determination.
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Computerized analysis schemes are being developed to aid in distinguishing between
malignant and benign lesions in order to improve both sensitivity and specificity. Such methods
may use features extracted either by computer or by radiologists. The benefit of computerextracted features is the objectivity and reproducibility of the measure of the specific feature.
However, radiologists employ many radiographic image features, which they seem to extract and
interpret simultaneously and instantaneously. Thus, the development of methods using
computer-extracted features requires, besides the determination of which individual features are
clinically significant, the computerized means for the extraction of each such feature.
It is important to restate that one of the aims of computerized classification is to reduce
the number of benign cases sent for biopsy. Such a reduction will be clinically acceptable only if
it does not result in unbiopsied malignant cases, however, since the "cost" of a missed cancer is
much greater than misclassification of a benign case. Thus, computer classification schemes
should be developed to improve specificity but not at the loss of sensitivity.
Various computerized classification systems are being developed using human-extracted
features (104-116) and computer-extracted features (117-130). The benefit of using computerextracted features is the objectivity and reproducibility of the result. However, radiologists
employ many radiographic image features, which they seem to extract and interpret
simultaneously and instantaneously. The development of methods using computer-extracted
features requires initial determination of which individual features are clinically significant, prior
to devising means for the extraction of each such feature. During the past ten years, large growth
has been seen in the development of computerized classification methods for lesions on breast
images. Most methods are evaluated in terms of their ability to distinguish between malignant
and benign lesions, however, a few have been evaluated in terms of patient management (i.e.,
return to screening vs. biopsy).
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Ultrasound and magnetic resonance images of the breasts are also being subjected to
computerized analysis to help distinguish between cancerous and non-cancerous lesions (131136 and 138-140, espectively). Breast sonography is used as an important adjunct to diagnostic
mammography and is typically performed to evaluate palpable and mammographically identified
masses in order to determine their cystic vs. solid nature. The accuracy of ultrasound has been
reported to be 96-100% in the diagnosis of simple benign cysts. Masses so characterized do not
require further evaluation. Breast sonography, however, has not been routinely used to
distinguish benign from malignant solid masses because of the considerable overlap in their
sonographic appearances. Magnetic resonance imaging (MRI) may aid in the diagnosis of breast
cancer as a complementary technique to mammography. MRI has the benefit of yielding threedimensional information of the breast. Contrast-enhanced MRI of the breast is used to indicate
differences between lesions and normal tissue due to the increased vascularity and capillary
permeability of tumors. However, some benign lesions are also enhanced with contrast-enhanced
MRI, and thus, the specificity of the technique is limited. Mussurakis et al. (137) reported that
significant variability exists in the assessment of lesions by humans using MR images. With
MRI, both temporal and spatial features of lesions may be useful in the discrimination task.
V. COMPUTERIZED BREAST CANCER RISK ASSESSMENT
Breast cancer risk assessment is expected to aid in appropriate surveillance plans that may
include enhanced screening for women at increased risk of breast cancer. Computerized methods
are being developed to relate characteristics of mammographic parenchymal patterns to breast
cancer risk. Mammographic density has benn shown to be strongly related to risk of breast
cancer (93). Computerized analysis of mammographic parenchymal patterns may provide an
objective and quantitative characterization and classification of these patterns (141-148).
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VI. CLINICAL EXPERIENCES AND COMMERCIAL SYSTEMS
The ultimate evaluation of CAD is not how well the computerized method performs in its
task, but rather how well the human performs when the computer output is used as an aid.
Observer studies have shown that radiologists' performance increased when using a computer
output as an aid -- in detection (36,45) and in diagnosis (127). It is important to note though that
the type of observer may affect the reported effect of CAD. One needs to determine and report
the amount of experience of the observers and their current mammography reading load. In
addition, it is important to consider the effect of disease prevalence on the observer study. For
example, in screening mammography, typically less than 1% of the cases will result in cancer.
One method of evaluating a CAD method is to determine its performance on a database
of missed lesions. Schmidt et al. (158) conducted a retrospective analysis using computer
analyses of a series of mammographic lesions missed in routine clinical practice. With a
database of 69 missed cases, the computer found 50% of the lesions with a false positive
detection rate of approximately 1.3 per image total for both microcalcification cluster and mass
detection algorithms (with a range of 0 to 11 false detections per image ). Similar.results from a
missed lesion study were found by te Brake et al. (159). In their study, the routine clinical
examination had included double reading. Twenty-two of 65 cases were detected at one false
positive finding per image.
It should be noted that the use of CAD does not require a "digital" radiology department.
Most likely, primary diagnosis will still be made from original mammographic films for many
years. The design of a dedicated CAD workstation would incorporate detection schemes for both
clustered microcalcifications and masses. Appropriate man-machine interfaces must be
developed for the effective and efficient implementation of these CAD schemes. The basic
hardware of the workstation could include a laser film digitizer (or an interface to a digital
mammographic system), a high-speed computer, a mechanism for CAD implementation and an
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optional link to PACS (picture and archival communications systems). Possible mechanisms for
CAD reporting include (1) low-resolution image paper printer or monitor with primary diagnosis
from original films, (2) high-resolution film printer with primary diagnosis from original films
(or possibly the printed film), or (3) multiple high-resolution monitors with primary diagnosis
from the CRT screen. The computer output may be presented to the radiologists either by
symbols (arrows, circles, etc.) that may be toggled on and off, or by simple text indicating the
location of possible lesions in the mammogram. Radiologists would use the computer-reported
results as aids in making their final diagnostic decision.
In the clinical setting, CAD methods might be used as a test invoked by the radiologist
upon viewing a particular mammographic case or as a routine screening procedure performed on
all examinations. The workstation could be configured to allow the radiologist to control the
sensitivity and specificity of the computer output. Obviously, a choice of fewer false-positive
lesions would be achieved at the cost of a lower number of true-positive lesions, and vice-versa.
This trade-off could be adjusted by the radiologist, depending on the nature of the case material
and personal preference. For example, a radiologist might choose an output with high sensitivity
for examining high-risk patients being screened for cancer, whereas a lower sensitivity and
correspondingly lower false-positive rate might be desired for patients at low risk for cancer.
Since November 1994, researchers at The University of Chicago have been analyzing
screening mammograms on an "intelligent" workstation for use as a "second opinion" (160,161).
This large-scale evaluation of computer-aided diagnosis in mammography in a clinical
environment is being conducted with the CAD code “frozen” since 1994, and to date, over
19,000 mammographic screening cases have been analyzed by the computer system. The four
standard screening mammograms for a given case are digitized and analyzed by the system. In a
prospective study of 10,000 consecutive screening mammograms (using the 1994 version of the
detection methods), the computer indicated cancer about one year before it was clinically
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diagnosed in approximately 15% of all cancer cases and in 56% of cases where the cancer was
visible in retrospect on a clinically negative-read screening mammogram (161). The computer
had a false-positive rate of approximately 1.3 false clusters per image and 2.1 false masses per
image. It should be noted that on a subset of cases in which the radiologist used the computer
output , no change in call back rate occurred.
Hendricks (162) of the University of Nijmegen compared use of the ImageChecker system
(R2 Technology, Inc., Los Altos, California) with double reading with two radiologists. They
reported no difference between double reading and CAD.
Sittek and Reiser (38) reported on their initial experience with the ImageChecker system at
the University of Munich in which 82% of the malignant lesions were detected by the computer.
They found that CAD analysis can be implemented into an existing mammography section
without disturbing patient handling. They also noted that use of the system prolonged the patient
waiting time by approximately 15 minutes, but felt the extra time was “well worth it”. Currently,
due to the false positive rate of the ImageChecker, the investigators reported that the computer
marks need to be rechecked by an experienced radiologist. They do expect, however, that use of
such a CAD system will increase detection for lesser-experienced radiologists.
A large study (163, 165) involving a retrospective analysis of 1083 consecutive cancer
cases with 13 institutions and more than 24 radiologists was performed as part of a FDA
approval process for the ImageChecker (R2 Technology, Inc., Los Altos, California). The
sensitivity for microcalcification cluster detection was 98.3% and that for mass detection was
72% with an average false positive rate (cluster and mass combined) of one per image. A
prospective component of their study included an analysis of 14,817 cases with the CAD system.
No statistically significant change was observed in the radiologists’ workup rate when the system
was used as a screening tool.
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Results from these various “clinical” studies are quite encouraging, thus leading to
continued research in CAD and the arrival of more commercial systems (e.g., SecondLook from
CADx and Scanis System from Trex Medical), which may seek FDA approval.
VI. DISCUSSION and SUMMARY
Overall CAD systems have been shown to 1) detect lesions missed by radiologists, 2) not
affect the recall rate in screening mammography, 3) exhibit sufficient robustness to changes in
image acquisiton and digitization, 4) integrate well into pre-existing mammography sections, and
5) classify lesions similar to expert mammographers.
As further improvements in the performance of computer methods are obtained, further
testing of systems in the clinical environment needs to be done. Due to the success of
computerized classification in observer performance studies, plans should include the integration
of detection and classification methods into more clinical prototypes. In addition, with the
advent of direct digital mammography systems, CAD will be directly incorporated into systems
and tested. Investigators should also take advantage of the digital image data from
mammographic, sonographic, and MR images of the breast, thus, allowing for the integration of
image information from multimodalities.
For the present, it is important to appropriately report CAD results so that they are useful
to the research community. When presenting performance results of a computerized image
analysis method, the following should be clearly described: (a) acquisition of the digital image
and image quality, (b) characteristics of the database including method of verifying diagnostic
truth, (c) use of the database(s) with respect to development and testing, (d) method of scoring,
(e) type of performance measures, and (f) performance as an aid to observers.
The ultimate test of any computerized analysis scheme will be its ability to actually
improve radiologists' accuracy when used as an aid. Since, in CAD, the computer is used as a
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second opinion and not alone, it may not need to be perfect. It should be noted that a CAD
scheme will be useful even at a less-than-perfect sensitivity, especially if the mammographic
lesions detected by the computer do not overlap completely with those detected by a radiologist.
However, a high sensitivity, at the likely cost of some acceptable number of false positives, is
preferred since false negatives in the screening for breast cancer are significantly worse in terms
of clinical outcome than are false-positive diagnoses. It is important to note that currently
computer analysis schemes are being used as “second readers” and not as primary readers.
Over thirty years after Winsberg et al. (77) first reported on a computerized method for
mammograms, CAD has become a recognized and accepted component of future breast imaging
as evidenced by its clear presence at the annual meetings of AAPM and RSNA, and by the fact
that a commercial CAD system for mammography has had FDA approval for three years and is
in routine clinical use. A systematic and gradual introduction of CAD into radiology
departments is necessary so that minimum disruptions occur in current reading habits, thereby
insuring the acceptability of CAD and optimal diagnostic performance by the radiologist.
The information technology revolution is now changing methods of interpretation of
mammographic images and these changes are being enthusiastically embraced by both the
medical profession and the public (patient). This progress can be attributed to a variety of
reasons including the following:
•
Digital image quality has improved considerably.
•
Computers are faster, have sufficient storage capabilities, and are able to efficiently
accommodate large images over networks.
•
Investigators have realized that the development of robust methods requires large databases
of clinical images, which have become more feasible to collect.
•
There exists more focussed attention to current and new computer vision techniques.
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•
Radiologists/clinicians have recognized the medical necessity of computerized assistance
especially in settings such as screening for cancer.
•
Investigators are sensitive to the constraints imposed by the end user, e.g., the radiologist.
•
Public awareness of computer applications in medical imaging has increased.
This is just the beginning. In the future, potentially all medical images will have some form
of computer analysis performed on them in order to benefit the diagnosis.
ACKNOWLEDGEMENTS
The author is grateful to various members of the Kurt Rossmann Laboratories for Radiologic
Image Research for useful discussions. This research was supported in parts by USPHS grants
CA60187, T32 CA09649, and RR11459, and U.S. Army Medical Research and Materiel
Command (DAMD 17-96-6058, 17-97-1-7202, 17-98-1-8194). M. Giger is a shareholder in R2
Technoloy, Inc. (Los Altos, CA). It is the University of Chicago Conflict of Interest Policy that
investigators disclose publicly actual or potential significant financial interests which would
reasonably appear to be affected by the research activities.
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