Computer-aided detection (CAD) software can be used to help radiologists address the ever-increasing number of images that need interpretation. With advancements in computed tomography (CT), hundreds of thin-slice images are now produced for a chest CT study. This article discusses the use of CAD software tools for chest CT and, specifically, their use in low-dose lung cancer screening, diagnosis of acute pulmonary embolism, and evaluation of interstitial lung disease.
is an Associate Professor of Radiology, Department of Medical
Imaging, University of Toronto/University Health Network,
Computer-aided detection (CAD) software tools have been
developed to support radiologists and help them cope with the
ever-increasing number of images that need interpretation. The
demand for computer assistance emerges from both increasing
indications and technologic developments. The prototypical example
of increasing CAD indications is mammography. Screening mammography
is recommended in a large number of patients, and, even though the
number of images per patient remains limited, the number of studies
is continually rising. As such, mammography has been the first area
in which companies have developed CAD software and, to date, most
are working on this indication. Technologic advances, particularly
the development of multidetector computed tomography (MDCT), permit
CT of the chest with thin, overlapping axial slices, which results
in several hundred images per study. In chest imaging, the demand
for CAD has grown with new MDCT indications, such as low-dose lung
cancer screening. More recently, CAD has been applied to
established areas of chest CT, such as the diagnosis of acute
pulmonary embolism (PE) and interstitial lung disease.
This article addresses CAD software tools in use for chest CT.
Several companies have CAD software in different stages of
development. Based on the findings of a multireader study,
R2 Technology, Inc. (Sunnyvale, CA) was the first company to
receive FDA approval for a lung CAD product, ImageChecker CT CAD
Software System. This software targets the detection and follow-up
of lung nodules and the detection of pulmonary arterial filling
defects. Medicsight (London, England) offers LungCAD, Siemens
Medical Solutions (Malvern, PA) has
LungCARE, and Philips Medical Systems (Bothell, WA) has a CAD
product in development that is not yet commercially available in
North America. Other companies are also developing CAD software for
lung CT or digital radiography, including MEDIAN Technologies, Inc.
(Brookfield, WI), EDDA Technology, Inc. (Princeton Junction, NJ),
and Riverain Medical (Miamisburg, OH). The author has ac-cess to
the most recent software releases from R2 Technology and
Medicsight; information from the other vendors is available in the
Assessing the performance of CAD
In general, CAD results are displayed as prompts or markers on
the pre-existing axial CT images (Figures 1 and 2) or 2-dimensional
(2D) or 3-dimensional (3D) reconstructions of the lungs (Figure 1).
These marks, most commonly in the form of a circle, draw the
reader's attention to a suspected target lesion. The CAD
performance is assessed by how much its use improves the
radiologist's diagnosis of the target lesion while maintaining or
even reducing interpretation time and thus improving workflow. The
CAD algorithms are "trained" to select target lesions-those found
and those missed by the radiologist. The sensitivity of CAD is
determined by the number of true-positive results divided by the
number of true-positive and false-negative (missed) findings. If
all lesions are found by CAD, (ie, if there is a high number of
true-positive CAD markings), its sensitivity is high.
Traditionally, sensitivity has been used in the literature as a
performance measure for CAD.
However, the requirement for high sensitivity is actually dependent
on the way in which CAD is used--as a second reader or as a
If CAD is used as a second reader (as R2 Technology, for
example, promotes the use of their product), radiologists tend to
use the CAD system to indicate nodules they may have overlooked,
rather than as an adjudicator for questionable nodules.
As such, it is not necessarily required that all target lesions are
found; the overall sensitivity might be quite low yet still be
sufficient as long as the lesions that were missed by the
radiologist are found.
In the second-read model, sensitivity might not be a sufficient
performance measure for CAD. A second read with CAD inevitably
increases the interpretation time for the radiologists and impairs
If, on the other hand, CAD is used as a concurrent reader (as
Medicsight's product is promoted), sensitivity generally needs to
be much higher. If radiologists immediately have CAD marks
available, they could rely on CAD's ability to find lesions and may
decrease their attention. On the other hand, this simultaneous CAD
reading has a much lower impact on interpretation time and
The impact on interpretation time is not only affected by the
second versus concurrent reading approach, but also by the number
of false-positive readings, which are defined as any CAD marks that
are not target lesions. The rejection of false-positive marks
increases the radiolo-gist's interpretation time, negatively
impacting overall reading time and workflow. The term
is generally not defined in the assessment of CAD performance,
because there is no gold standard that shows all nodules.
Consequently, the specificity of CAD is not caluclated. However, it
has been suggested that the term
be used to describe a case with no lesions for which no CAD marks
are generated. This definition would potentially be useful if a
first-reader paradigm were ever implemented. In this paradigm, the
CAD would be applied before the radiologist reading; in cases in
which no CAD marks were generated, the radiologist would not have
to perform a detailed nodule search. Clearly, this requires close
to 100% sensitivity for CAD, and no system has yet reached this
level of performance. Nevertheless, this would be a promising CAD
implementation, particularly for screening databases. While it is
already difficult to define the parameters to describe CAD
performance, the re-quirements for CAD performance vary with the
database under evaluation and with the experience of the user.
The database under evaluation determines the need for sensitive
detection of small nodules. In screening cases, nodules <5 mm
are unlikely to be of clinical significance,
and a low sensitivity for such small nodules may be acceptable. On
the other hand, any nodule (particularly a new nodule) in an
oncologic patient is primarily suggestive of a metastasis and must
be detected regardless of its size; therefore, a high sensitivity
is required even for small nodules in these patients. To date, most
CAD systems have a detection rate that decreases with decreasing
This effect and the larger number of false-positive results
contribute to impaired CAD performance when targeting smaller
nodules. Since a radiologist's performance also deteriorates when
he or she is looking for small nodules, there is still an overall
incremental improvement in diagnostic accuracy even when CAD is
used for small nodule detection.
Reader experience is another factor that influences the
evaluation of CAD software. If nonexperienced readers interpret a
chest CT, they might appreciate any mark pointed out by the CAD, as
they are more likely to miss a target lesion than would a more
experienced radiologist. They might not mind the additional time
required to interpret the study. In a study of the incremental
effects of CAD on the performance of readers with different levels
of experience, there was a significant difference in detection
rates between radiologists and nonradiologists before CAD; but,
after CAD, there was no significant difference in detection rates
between these readers.
In other words, the effect of CAD on sensitivity was larger in an
inexperienced reader. This tendency helps support the use of CAD to
assist a relatively inexperienced on-call resident to identify
pulmonary arterial filling defects. Given these factors, a good CAD
performance can be defined as a high sensitivity (de-tection rate)
combined with a low number of false positives.
CAD of pulmonary nodules
To date, the use of CAD in chest CT has been focused primarily
on the detection of pulmonary nodules. Most manufacturers develop
software for this indication, and many studies have been published
in this area.
The target lesion or true-positive CAD mark is any pa-renchymal
nodule, benign or malignant (Figure 3); false-positive marks would
be any other anatomical or artifactual structure that is not a
nodule. False-positive findings are to be expected from artifacts
from respiratory or cardiac motion, vessel bifurcations, hilar
vessels, and parenchymal scars.
The author has had experience with the ImageChecker software
from R2 Technology, which is designed as a second reader to be
implemented after the radiologist's initial read. In an analysis of
250 low-dose CT scans (60 mA, 140 kV, 1.25-mm slices) from a lung
cancer screening study, the radiologists found 83 nodules.
The CAD system found an additional 21 nodules that had been
previously missed in the radiologists' read. Thus, the radiologists
had a sensitivity of 80% (83 of 104). Overall, CAD found 76
nodules, for a sensitivity of 73% (76 of 104). This result was
based on noncalcified, solid nodules with a cutoff size of 5 mm. In
this study, the results of CAD and the radiologists' results are
complementary, since the use of CAD as a second reader tends to
find some nodules that are different from those that the
radiologist identifies and can indeed improve lung-nodule detection
in a screening population.
The complementary results of a radiologist's read and CAD
results have also been found by other studies.
On the other hand, in the author's study, 739 CAD entries were
false positives, which was 86% of all CAD entries for an average of
0.01 per section, or 3 per patient.
Most false-positive results fell into the expected categories
(noted above), but there were also marks made in normal mediastinal
organs and in osteophytes (Figure 4). All false-positive marks were
easily dismissed by glancing at the image or scrolling up and down
a few anatomic levels, but this still required a considerable
amount of time for the second read with CAD. Clearly, the CAD
software cannot yet achieve the differentiating ability of the
radiologist's "glance" in these cases.
There is general agreement in the literature that the addition
of CAD improves a radiologist's nodule detection rate.
If a slightly different approach is used to evaluate the
incremental improvement when CAD is used in cases that were
previously reported as normal,
or in cases of previously missed cancers,
results show that CAD improves diagnostic yield.
However, individual studies are difficult to compare, since they
have been performed with different CAD systems, on different
databases, using different CT scanning parameters, and with
different size thresholds for computing sensitivity and false
positives (Table 1). Lee et al
studied the influence of radiation dose on the use of CAD and
reported that a decrease in dose results in a higher false-positive
rate. In order to create a standardized image database of MDCT lung
images as a resource for CAD researchers, the National Cancer
Institute has formed the Lung Image Database Consortium (LIDC).
This database will be available to researchers and is expected to
lead to the publication of comparative studies.
Most published studies to date have used the second-read model.
If CAD is designed as a concurrent reader, as in Medicsight's
LungCAD, CAD highlights potential areas of nodules that the
radiologist must accept or dismiss in real time. The challenge with
this approach is to limit the number of false-negative marks to an
absolute minimum. When CAD is used for joint reading, a radiologist
will be more likely to rely on the computer algorithm to point out
any potential nodule and, thus, would be expected to decrease their
effort to detect additional nodules not pointed out by the
software. A recent study supports this expectation.
Comparing both sensitivity and reading time when CAD was used
simultaneously and as a second reader, the mean sensitivity was 68%
for reading without CAD, 68% for concurrent reading, and 75% for a
second reading. The mean reading time without CAD was 294 seconds,
was reduced with concurrent reading (274 seconds), and was longer
with a second-read approach (337 seconds).
Most of these results confirm the expected effects of concurrent
CAD reading. Interestingly, the authors found that sensitivity was
unchanged when they compared reading without CAD (68%) with reading
concurrently with CAD (68%). This is likely explained by the
decrease in attention of the radiologist during concurrent CAD
application, which is supported by the decreased reading time. The
authors concluded that CAD could either decrease interpretation
time or improve nodule detection, but not both. These results
"CADx" of pulmonary nodules
In addition to the detection of pulmonary nodules, a second area
of potential CAD application is for the characterization of nodules
as possibly malignant or likely benign lesions. With the current
use of MDCT, the sensitivity for the detection of lung nodules is
high, but the specificity for diagnosing malignant nodules is low.
Additional features must be included in CAD tools to detect
malignant nodules, the so-called CADx software tools,
which would point out malignant nodules only and would allow
true-negative values and, hence, specificity to be computed.
CADx software, in general, has a variety of indications,
including the quantification of microvascular parameters derived
from con-trast-enhanced dynamic CT perfusion studies
and the quantification of positron-emission tomography (PET) data.
With the use of CADx in MDCT scanning, analysis options are based
on a more detailed evaluation of morphology or, if studies from
several time-points are available, the evaluation can quantify any
nodule growth. Incorporating morphology into decision analysis
seems to be the most basic approach. Ideally, morphologic features
(such as shape, density, and location) would be used to rank a
nodule into cancer probabilities and display them with different
symbols. Li et al
successfully trained CADx software to determine the likelihood of
malignancy of lung nodules based on various objective features,
which confirmed this promising approach.
The inclusion of nodule growth has been evaluated in more
detail. Most vendors offer a "temporal comparison" tool that
displays any change in any given nodule, which is commonly reported
as growth rate in days and as percent of volume change (Figure 5).
This information seems to be highly valuable for the
characterization of nodules as possibly malignant or likely benign,
and, moreover, provides prognostic information in the case of
cancer. This information may also be useful in monitoring treatment
responses. Thin-slice (1 to 1.25 mm) MDCT scanning protocols with
isotropic voxels are used in displaying a nodule, which may allow
accurate measurements of 3D lung-nodule volumes. The volume
approach promises to be more sensitive to change than the
previously used diameter measurements. The actual doubling of a
sphere volume would imply a diameter change of only 26%, which can
easily be overlooked, particularly in smaller nodules. The most
commonly used threshold for a benign lesion is a volume doubling
time (VDT) >400 days, which is equivalent to the absence of
lesion growth in a 2-year period. An average VDT in lung cancer has
been reported to be 163.7 days.
This definition has been challenged in the literature and might
need to be revised. In screening populations, the mean VDT is
higher (mean 452 ± 381 days, range 52 to 1733 days), which is
attributable to a higher proportion of slow-growing adenocarcinomas
in screening populations.
Morphologic considerations, particularly the density or proportion
of ground-glass opacities, should be used in combination with
In addition to the limited definition of a VDT that indicates
malignancy, there are several technical problems that can impair a
correct volume measurement. Even assuming thin-slice MDCT scanning
with consistent parameters, volume measurements are influenced by
attached structures (such as vessels and pleura) that might be
included in the volumes to different extents and by different
Most of the preliminary studies have concluded that, for solid
nodules, only computer-assisted volumetry is accurate, robust,
repeatable, and consistent.
However, this must be validated, and reliable thresholds for the
definition of growth must be defined.
In addition to comparing the volume of nodules on studies from
different time points, CADx tools offer automated registration and
nodule matching to decrease the time required for comparison.
This approach is impaired by changes in respiratory state, patient
po-sition, and possible interval changes in lung anatomy due to
surgical or other factors, such as infection. According to the
author's own unpublished experience, this approach can result in
misregistrations that frequently require "unlinking" of the current
and former CT scans, which makes automated registration still
somewhat impractical (HC Roberts, unpublished data, 2006).
Current limitations of nodule CAD and CADx in clinical
Most CAD studies have been performed with thin slices (1 to 2
mm). Some algorithms allow for the processing of thicker (5 mm)
slices, but others do not. For example, the ImageChecker algorithm
will not execute if slices are thicker than 3 mm. Thicker slices (5
to 10 mm) allow partial volume effects if the nodules are smaller
than the slice thickness, which results in an apparent subsolid
density. Increasing the slice thickness decreases the sensitivity
for nodule detection.
Since the algorithm is designed not to reject subsolid foci, even
more false-positive entries result
and small nodules may be obscured by adjacent vessels.
Fiebich et al
conducted a study of the use of CAD in 10-mm slice CT scans and
reported sensitivities of 38% with 6 false positives per patient or
72% sensitivity with false positives per patient. Similarly, volume
assessments are quite inaccurate due to the partial volume effect
of small nodules, making accurate growth analyses impossible.
Most clinical protocols, however, still include a 5-mm slice
thickness. A general change in protocol would have an unacceptable
impact on picture archiving and communication systems (PACS) and
radiologists' workload. Consequently, such CAD approaches should be
used only with thin-slice protocols, not in routine clinical
settings, if CT scans with slice thicknesses ≥5 mm are
CAD for the analysis of diffuse lung disease
The assessment of diffuse, interstitial lung disease is a major
clinical topic in chest CT but has not caught the interest of CAD
developers. In the same way that software tools help to detect lung
nodules, CAD could help to detect diffuse lung disease, and CADx
could help to characterize and define the type of disease. Uchiyama
studied the use of CAD and reported a sensitivity of 99.2% for
identifying any abnormal lung patterns (ground-glass opacities,
reticular or linear opacities, nodular opacities, honeycombing,
emphysematous changes, or consolidation). They also reported a
specificity for a normal area of 88.1%. Although these early
results suggest that CAD may eventually be able to assist
radiologists in their assessment of diffuse lung disease, the
necessary software tools are still in the theoretical development
CAD for detection of vascular filling defects
Contrast-enhanced CT scans have become the standard tool for the
detection or exclusion of PE. Given the high number of patients
with suspected PE and the ever-increasing number of CT angiograms
performed with several hundred sections per scan, CAD is expected
to improve the accuracy and efficiency of radiologists'
interpretation. Target lesions or true-positive CAD marks include
any vascular filling defect (Figure 6). False-positive marks would
be those outside or within a patent pulmonary artery. The
exists in the assessment of PE; thus, specificity can be
R2 Technology was the first company to launch a CAD tool that
assessed pulmonary artery patency. Their new Pulmonary Artery PE
Tool can be used with the ImageChecker CT software and is designed
to help physicians detect potential filling defects such as emboli.
Das et al
studied the use of this tool in CT scans that were positive for PE.
They reported a CAD sensitivity of 88% for segmental PE and 78% for
sub-segmental PE, with an average of 4 false-positive CAD marks per
case. Zhou and colleagues
tested proprietary software on a similar set of CT scans that were
positive for PE. They reported sensitivities of 92% for proximal PE
and 77.8% for subsegmental PE, with an average of 18.3
false-positive CAD marks per case. The case sensitivity was 92.9%.
The author participated in the study by Colak et al
that assessed the utility of a first-generation CAD algorithm
(ImageChecker CT) for pulmonary arterial filling defects in an
unselected group of 100 patients who subsequently underwent CT
angiograms performed to exclude PE. All scans were performed with a
1- or 1.25-mm slice thickness. Sensitivity for a positive or
negative result was 67% (of 18 PE positive scans, 12 had at least 1
CAD mark) and specificity was 55% (of 82 PE negative scans, 37 had
at least 1 CAD mark). Unfortunately, the false-positive CAD marks
are not as easily dismissible as they are in the case of lung
nodule CAD. In this study, the majority of marks were in pulmonary
veins (which accounted for 75% of all false-positive CAD marks),
frequently in the periphery, and they required correct anatomic
localization (Figure 7). The positive-predictive value was 24%, and
the negative-predictive value was 88%. Given the high
negative-predic-tive value, there seems to be immediate utility and
important practical relevance of this software, even in its first
version, to help junior radiology residents exclude PE. Using CAD,
resident interpretation of pulmonary CT angiograms increased from
an average of 4.5 minutes per case to 5 minutes per case; however,
the in-terpretation confidence also increased. Without CAD, the
resident indicated little confidence in the result in 10 cases,
moderate confidence in 17, and high confi-dence in 78 cases. With
CAD, the confidence numbers were 5 (little), 21 (moderate), and 77
Resident diagnostic accuracy also improved. Without CAD, the
presence or absence of PE was correctly reported in 88 cases with 4
false-positive results and 6 false-negative results. With CAD, 91
cases were reported correctly, with 3 false-pos-itive and 4
false-negative results. The next iteration of
vascular-filling-defect CAD is available and has been tested.
Developers of CAD software face a great challenge in creating
products that will mark enough suspicious areas to enhance the
radiologists' interpretation without overwhelming the reader with
false-positive marks. The challenge is even greater since the
determination of how many marks are "enough" without being
"overwhelming" is highly subjective. This may cause some readers to
react emotionally with frustration and, perhaps, reject the
product. Assuming that the future of chest CT CAD resembles the
development of CAD in mammography, we are not likely to see early,
widespread use, and there is no immediate danger that CAD will
replace the radiologist.
When all of these issues are resolved, when the influence of
radiation dose and slice thickness on CAD performance are clear,
and when comparison thresholds are defined and morphologic features
incorporated, developers will still need to have their products
integrated into existing PACS reading workstations before
widespread use is likely. But, even at this point in thoracic CAD
development, the benefits of CAD systems- as concurrent or second
readers-seem worth the effort required to overcome these