DOI:10.2214/AJR.07.2057
AJR 2007; 189:1077-1081
© American Roentgen Ray Society
Performance of a Computer-Aided Program for Automated Matching of Metastatic Pulmonary Nodules Detected on Follow-Up Chest CT
Kyung Won Lee1,2,
Miyoung Kim1,
David S. Gierada1 and
Kyongtae T. Bae1,3
1 Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St. Louis, MO.
2 Present address: Department of Radiology, Seoul National University School of
Medicine, Bundang Hospital, Seoul, Korea.
3 Present address: Department of Radiology, University of Pittsburgh School of
Medicine, 200 Lothrop St., Suite 4895, Pittsburgh, PA 15213.
Received February 16, 2007;
accepted after revision June 7, 2007.
Address correspondence to K. T. Bae
(baek{at}upmc.edu).
Abstract
OBJECTIVE. The purpose of this study was to evaluate the performance
of a computer-aided program that allows automated matching of metastatic
pulmonary nodules imaged with two serial clinical chest CT studies.
MATERIALS AND METHODS. The cases of 30 patients with metastatic
pulmonary nodules depicted on two serial clinical MDCT scans (16- or 64-MDCT,
5-mm section thickness) were studied. The number of nodules per patient varied
from a minimum of two to innumerable. A maximum of 10 well-defined solid
nodules per patient, a total of 210 nodules, were selected from each baseline
CT scan and were evaluated for matching detection in follow-up CT by means of
an automated program. Substantial changes in lung findings and lung volumes
between serial scans were visually assessed. The effects on matching rate of
interval lung changes and location, size, and total number of nodules in the
lung were analyzed with contingency tables. Chi-square tests were used to
evaluate patterns for statistical significance.
RESULTS. The nodule-matching rate per patient ranged from 0 to 100%
(median, 87.5%). By nodule, the overall matching rate was 140 of 210 (66.7%).
Matching rate was highly associated with changes in lung quality between
serial studies. Matching of 122 of 148 nodules (82.4%) occurred in 23 patients
with relatively unchanged lung findings, compared with 18 of 62 nodules
(29.0%) in seven patients with substantial interval changes (p <
0.001). The matching rate decreased with an increased total number of nodules
per lung. For 10 or fewer nodules per lung, matching was successful for 31 of
36 nodules; for 11–50 nodules per lung, 60 of 73 nodules; for
51–100 nodules per lung, 33 of 47 nodules; and for more than 100 nodules
per lung, 16 of 54 nodules (p < 0.001). The matching rate was not
significantly different with location or size of nodules.
CONCLUSION. The rate of automated matching of metastatic pulmonary
nodules on clinical serial CT scans was high (82.4%) when the lung findings
and lung expansion between the serial scans were relatively unchanged. The
rate decreased significantly, however, with substantial interval changes in
the lung and a larger number of nodules.
Keywords: chest CT • computer-aided diagnosis • oncology • pulmonary nodules
Introduction
Chest CT is the most sensitive diagnostic imaging technique for the
detection of lung nodules [1].
CT techniques have been applied to screening for lung cancer in high-risk
populations and have been shown to be promising for the detection of lung
nodules
[2–6].
Various computer-aided detection systems have been proposed to improve the
detection of pulmonary nodules from CT images
[7–12].
These systems allow detection of pulmonary nodules with high sensitivity and
relatively low false-positive rates.
The lung is a frequent site of metastatic disease that manifests as
pulmonary nodules. With sequential follow-up CT scans, changes in nodule size
and number can be assessed
[13,
14]. Evaluation of disease
progression and treatment response, however, requires exact matching and
precise quantitative analysis of nodules
[15]. This task can be
facilitated with automated matching and temporal assessment computer programs,
which can improve the diagnostic performance and efficiency of
radiologists.
Results of evaluations of software programs for the automated localization
of pulmonary nodules on follow-up chest CT have been reported
[14,
16,
17]. Although the studies
emphasized the potential benefits of real-time automated matching for the
follow-up of lung nodules on CT images, to our knowledge the performance of
automated computed-aided matching has not been evaluated systematically for
association with interval lung changes and nodule characteristics that may
affect performance. The purpose of this study was to evaluate the performance
of a computer-aided program used for automated matching of metastatic
pulmonary nodules imaged in two serial clinical chest CT studies.
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Fig. 1A —50-year-old man with fewer than 50 metastatic nodules from
renal cell carcinoma and successful matching of well-circumscribed parenchymal
nodule in lingula. Transverse (A) and coronal reformatted (B) CT
images show baseline findings.
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Fig. 1B —50-year-old man with fewer than 50 metastatic nodules from
renal cell carcinoma and successful matching of well-circumscribed parenchymal
nodule in lingula. Transverse (A) and coronal reformatted (B) CT
images show baseline findings.
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Fig. 1C —50-year-old man with fewer than 50 metastatic nodules from
renal cell carcinoma and successful matching of well-circumscribed parenchymal
nodule in lingula. Transverse (C) and coronal reformatted (D) CT
images show follow-up findings. Overall matching rate was 100% (10/10).
Although all nodules had enlarged, number and distribution of nodules were
stable at similar inspiration levels.
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Fig. 1D —50-year-old man with fewer than 50 metastatic nodules from
renal cell carcinoma and successful matching of well-circumscribed parenchymal
nodule in lingula. Transverse (C) and coronal reformatted (D) CT
images show follow-up findings. Overall matching rate was 100% (10/10).
Although all nodules had enlarged, number and distribution of nodules were
stable at similar inspiration levels.
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Materials and Methods
Institutional review board approval was obtained. Informed consent was not
required for this retrospective study, which was compliant with the Health
Insurance Portability and Accountability Act.
Patients
The study included 30 consecutively enrolled patients (11 men, 19 women;
age range, 24–80 years; mean, 57.4 years) with metastatic pulmonary
nodules depicted on two serial clinical chest CT studies. The primary sites of
malignancy were as follows: lung (n = 9), kidney (n = 4),
breast (n = 4), uterus (n = 4), prostate (n = 2),
colon (n = 1), rectum (n = 1), urinary bladder (n =
1), sphenoid sinus (n = 1), testicle (n = 1), mesentery
(n = 1), and unknown (n = 1). The mean interval between the
CT studies was 2.5 months (range, 10 days–5 months).
Pulmonary Nodule Selection
Only solid nodules larger than 3 mm and smaller than 2 cm in diameter were
included. No cavitary, ground-glass, or subsolid (i.e., partially solid and
partially ground-glass) nodules were present in the patient population. The
number of nodules per patient varied from a minimum of two to innumerable. Ten
patients had 10 or fewer nodules, nine patients had 11–50 nodules, five
patients had 51–100 nodules, and six patients had more than 100 nodules.
A maximum of 10 solid nodules per patient were sampled on baseline CT by a
radiologist with 10 years of experience in interpreting chest CT images,
resulting in a total of 236 nodules to review. We arbitrarily chose the
maximum of 10 nodules as a convenient sample size for inclusion of
representative nodules in each CT study. Nodules throughout the lungs were
selected arbitrarily but with consideration of representing nodules of varying
sizes and locations. Twenty-six of these nodules were excluded because on
follow-up CT they were either completely resolved or completely obscured by
surrounding infiltrates or atelectasis. The remaining 210 nodules were
included and evaluated for matched detection on follow-up CT performed with an
automated program.
CT
Routine contrast-enhanced clinical chest CT for metastasis evaluation was
performed with 120 mAs at 120 kVp and either a 16- or a 64-MDCT Sensation
scanner (Siemens Medical Solutions). The images were reconstructed with a
standard lung kernel at a section thickness of 5 mm with no intersection gap,
according to the routine clinical chest CT protocol at our institution.
Automated Matching of Pulmonary Nodules
Serial CT images were retrieved from the institutional PACS and sent to a
workstation (Leonardo, Siemens Medical Solutions) that contains an automated
matching program (LungCARE VB20, Siemens Medical Solutions)
[17,
18]. In brief, the
nodule-matching operation began with computation of approximate longitudinal
global alignment between the two serial sets of CT images. Refined alignment
parameters were then calculated on the basis of the cross-sectional area of
the lungs and the position of the trachea. Surface points of all surrounding
objects were extracted and used to produce a distance map. Points in the
follow-up set of CT images were superimposed onto the distance map for the
baseline set and then shifted in three directions in a search for the optimal
correlation between the two sets.
The performance of nodule matching with the program was assessed. When a
nodule on the baseline CT images was marked by an operator with a mouse click,
a volume of interest (VOI) surrounding the nodule was defined. At the same
time, the corresponding VOI was determined automatically on the follow-up CT
images (Fig. 1A,
1B,
1C,
1D). If the correctly matched
nodule was located within the VOI on the follow-up CT images, automated
matching was considered successful. In addition to the transverse images,
coronal reformatted images were displayed as a visual aid to assessment of
automated nodule matching.
Data and Statistical Analysis
Substantial interval changes in the lung findings (effusion, atelectasis,
infiltrates, and marked changes in disease state) or lung expansion between
serial CT studies were visually assessed by consensus of two radiologists with
10 and 11 years of experience in interpreting chest CT images. The patients
were divided into two groups, those with relatively unchanged and those with
substantially changed lung findings. Substantial changes included considerable
interval differences in lung expansion and the extent of lung cancer and
metastatic masses, lung infiltrates, pleural effusion, and atelectasis. The
rate of nodule matching was evaluated, and the rates for the two groups were
compared.
The patients were divided into four groups according to the total number of
nodules within their lungs: 10 or fewer nodules, 11–50 nodules,
51–100 nodules, and more than 100 nodules. The nodule-matching rates of
the four groups were compared. Each nodule was characterized according to its
location within the lung as parenchymal, peripheral, juxtaphrenic, or
juxtavascular. Parenchymal nodules were completely surrounded by aerated lung
parenchyma. Peripheral nodules were contacting or located within 2 mm of the
pleura. Juxtaphrenic nodules were in contact with the diaphragm. Juxtavascular
nodules were in contact with a blood vessel. The nodule-matching rates for
these four nodule locations were compared. The nodules were divided into two
groups according to size on baseline CT. The size (maximum diameter) of a
nodule was computed automatically from its segmented nodule volume with the
matching program. We arbitrarily used 10 mm as the size cutoff for the two
groups: nodules 10 mm or less and nodules greater than 10 mm in maximum
diameter. The nodule-matching rates of the two groups were compared.
We constructed contingency tables to compare differences in ability to
match nodules by patient and nodule characteristics. Patterns were tested for
statistical significance with chi-square tests. We used the program SPSS
version 12.0 for Windows (SPSS) for the statistical analysis.
Results
The nodule-matching rate per patient ranged from 0 to 100% (median, 87.5%).
By nodule, the overall matching rate was 140 of 210 (66.7%). Most (49/70) of
the unmatched nodules were found within three sections immediately above or
below the corresponding VOI determined and located by the program. Several
(21/70) unmatched nodules, however, were located in different segments remote
from the VOI assigned by the program.
We observed that the lung findings were relatively unchanged in 23 patients
but that substantial changes had occurred in the other seven patients:
markedly changed expansion of the lung (n = 1) (Fig.
2A,
2B,
2C,
2D), markedly changed extent of
lung cancer or metastatic masses (n =4) (Fig.
3A,
3B,
3C,
3D), and markedly increased
amount of pleural effusion with atelectasis (n =2). The matching rate
was highly associated with the changes in lung quality between serial scans
(p < 0.001): 122 of 148 nodules (82.4%) were matched in 23
patients with relatively unchanged lung findings, compared with 18 of 62
nodules (29.0%) in seven patients with substantial interval changes
(Table 1).
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Fig. 2A —53-year-old man with numerous metastatic nodules from renal
cell carcinoma and unmatched nodule in lingula. Transverse (A) and
coronal reformatted (B) CT images show baseline findings.
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Fig. 2B —53-year-old man with numerous metastatic nodules from renal
cell carcinoma and unmatched nodule in lingula. Transverse (A) and
coronal reformatted (B) CT images show baseline findings.
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Fig. 2C —53-year-old man with numerous metastatic nodules from renal
cell carcinoma and unmatched nodule in lingula. Transverse (C) and
coronal reformatted (D) CT images show follow-up findings. Overall
matching rate was 30% (3/10). Matched volume of interest (VOI) in C and
D is present in wrong place over lung parenchyma without including
nodule. Correct nodule (not shown) that should be matched was located two
sections superior to VOI. Matching rate is low, likely because of substantial
difference in inspiration levels between CT studies. (Inspiration level
difference is not evident in figures, which are screen capture images centered
at matching VOIs.)
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Fig. 2D —53-year-old man with numerous metastatic nodules from renal
cell carcinoma and unmatched nodule in lingula. Transverse (C) and
coronal reformatted (D) CT images show follow-up findings. Overall
matching rate was 30% (3/10). Matched volume of interest (VOI) in C and
D is present in wrong place over lung parenchyma without including
nodule. Correct nodule (not shown) that should be matched was located two
sections superior to VOI. Matching rate is low, likely because of substantial
difference in inspiration levels between CT studies. (Inspiration level
difference is not evident in figures, which are screen capture images centered
at matching VOIs.)
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Fig. 3A —47-year-old woman with numerous metastatic nodules from small
cell lung cancer in right lung and unmatched nodule in superior segment of
left lower lobe. Transverse (A) and coronal reformatted (B) CT
images show baseline findings.
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Fig. 3B —47-year-old woman with numerous metastatic nodules from small
cell lung cancer in right lung and unmatched nodule in superior segment of
left lower lobe. Transverse (A) and coronal reformatted (B) CT
images show baseline findings.
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Fig. 3C —47-year-old woman with numerous metastatic nodules from small
cell lung cancer in right lung and unmatched nodule in superior segment of
left lower lobe. Transverse (C) and coronal reformatted (D) CT
images show follow-up findings. Overall matching rate was 0 (0/10).
Infiltration of lung cancer was markedly increased, with complete collapse of
upper lobe of right lung during interval.
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Fig. 3D —47-year-old woman with numerous metastatic nodules from small
cell lung cancer in right lung and unmatched nodule in superior segment of
left lower lobe. Transverse (C) and coronal reformatted (D) CT
images show follow-up findings. Overall matching rate was 0 (0/10).
Infiltration of lung cancer was markedly increased, with complete collapse of
upper lobe of right lung during interval.
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The nodule-matching rate tended to decrease slowly with increasing numbers
of nodules but decreased dramatically when the number of nodules was greater
than 100 (p < 0.001 for the differences between the four
nodule-number groups). The matching rate was 31 of 36 (86.1%) for the group
with 10 or fewer nodules, 60 of 73 (82.2%) for the group with 11–50
nodules, 33 of 47 (70.2%) for the group with 51–100 nodules, and 16 of
54 (29.6%) for the group with more than 100 nodules
(Table 2).
The matching rates did not differ significantly with nodule location
(p = 0.87): 67.2% (88/131) for the parenchymal nodules, 62.5% (25/40)
for the peripheral nodules, 64.7% (11/17) for the juxtaphrenic nodules, and
72.7% (16/22) for the juxtavascular nodules. The matching rate also was not
significantly different between the large-nodule and small-nodule groups
(p = 0.27): 69.6% (87/125) for nodules 10 mm or smaller and 62.4%
(53/85) for nodules larger than 10 mm.
Discussion
Since the early 1990s, various computer-aided diagnosis (CAD) systems have
been evaluated for the detection of pulmonary nodules on CT images
[7–12,
14]. The sensitivity for
detecting nodules with these systems has varied from 38% to 100%, and the
number of false-positive detections per case has ranged from one to 75.
Although the results of many studies evaluating CAD systems for the detection
of nodules have been reported, automatic nodule matching and registration
programs for serial CT examinations have been scantly studied. Effective
automatic matching and quantitative analysis with CAD systems would be highly
useful for the evaluation of interval changes in nodules. Such automatic
matching is challenging owing to differences in rotation and translation of
the imaged structures. Additional difficulties arise in the automatic matching
of thoracic images as a result of differences in patient inspiration
[14,
17].
Results of several clinical evaluations of software for automated
localization of lung nodules on follow-up CT examinations have been published
[14,
16,
17]. The most recent study
[17] showed that the automatic
matching rate was 86.3% and that the matching rate was not affected by the
location or diameter of nodules. Compared with that rate, the overall
automatic matching rate in our study was lower, 66.7%. This discrepancy in the
matching rates is likely related to our study population, which included
patients with marked disease progression and with substantially variable
inspiration levels. This postulation may be supported by the fact that the
nodule-matching rate among patients with relatively unchanged disease extent
and inspiration level was 82.4%, comparable with the rate in the previous
study [17]. Our study differed
from the previous study in that we performed an additional evaluation and
found that the matching rate was significantly affected by interval changes in
disease and total number of metastatic nodules within the lungs.
Matching rate was highly associated with changes in lung quality between
serial CT studies. The matching rate was only 18 of 62 nodules (29%) in seven
patients with substantial interval changes. As a result, use of this automatic
matching algorithm should be confined to follow-up images with similar
conditions of surrounding lung disease and similar inspiratory states. This
algorithm would be expected to perform well in the population undergoing CT
for lung cancer screening because lung image quality and findings usually
remain stable between serial CT studies. The algorithm may have to be improved
further to achieve a high degree of matching in serial CT studies in which
large masses, marked interval changes, and innumerable metastatic nodules are
found.
In general, the sensitivity for nodule detection with CAD diminishes with
decreasing nodule size [11,
19]. The performance of CAD
systems in nodule detection also depends on the anatomic location of pulmonary
nodules [12]. In particular,
parenchymal and isolated nodules are more reliably detected than juxtapleural
and juxtavascular nodules, because parenchymal nodules are surrounded by
distinctly lower-attenuation pulmonary parenchyma
[12]. In contrast, the
performance of nodule matching with CAD is not influenced significantly by the
location or size of nodules, as found in our study. This discrepancy in the
performances of nodule detection and matching programs is not surprising.
Although the nodule detection tasks mainly involve detection and
differentiation of nodules from surrounding vessels, pleura, and airways,
nodule-matching tasks mainly depend on the search for and determination of
similar nodule structures within a predefined search space from a selected
nodule position. Therefore, some geometric features of nodules, such as size,
shape, and location, that are imperative for detection of nodules may be less
crucial in nodule matching.
In this study, the matching rate tended to decrease slowly with increasing
numbers of nodules but decreased dramatically when there were numerous nodules
in the lung. The low matching rate with a large number of nodules was caused
by the presence of unmatched nodules rather than by incorrect matching of
nodules. Although we are uncertain how the number of nodules within the lungs
affects the operation of the matching algorithm, our finding suggests that
registration of the coordinates between two serial CT scans is disturbed,
likely because many nodules occupy the lung parenchyma and obscure the normal
anatomic landmarks needed for registration. Another less likely possibility is
that the accuracy of the correspondence between two CT studies is reduced
owing to the presence of multiple matching candidates within the search
VOI.
For clinical applications, it appears that the current automated matching
program would allow faster assessment of whether metastatic disease has
changed than would assessment by manual matching of nodules. Although the
matching rates are reduced in some circumstances, the program seems to match
enough nodules to be of benefit in the sense that it can reduce the
radiologist's time spent determining whether change has occurred. Most of the
unmatched nodules were found within three sections of the nodule to be
compared, potentially reducing the search time even for cases in which a
nodule selected for follow-up comparison is not perfectly matched. Further
study is required for assessment of the clinical benefits of automated nodule
matching.
This study had limitations. First, it was difficult to objectively define
interval changes in lung quality. We relied on the consensus interpretation of
two chest radiologists for evaluation of lung quality, including any
differences in inspiratory levels between serial CT examinations. Second, only
well-defined solid nodules, up to 10 nodules per patient, were arbitrarily
selected and included in our study. No ground-glass opacity or alveolar
infiltrative or cavitary nodules were included. The detection and matching of
these nonsolid nodules likely would be more complicated, and further studies
should be pursued. Third, the clinical importance of nodule mismatching was
not assessed. This study was a retrospective evaluation of the performance of
an automated lung-matching program. We either identified mismatched nodules by
carefully comparing and searching for nodules between serial CT studies or
detected lung findings that caused nodule mismatching. This study was not
designed to compare the performances of the CAD program and the
radiologists.
In conclusion, the rate of automated matching of metastatic pulmonary
nodules in clinical serial CT scans was high (82.4%) when the lung findings
and expansion between the serial scans were relatively unchanged. The rate
decreased significantly, however, with substantial interval lung changes and
an increased number of nodules. Thus, in the current implementation,
application of the automatic matching algorithm seems most appropriate for
follow-up images obtained in cases with similar conditions of surrounding lung
disease and similar inspiratory states. The radiologist's attention to the
accuracy of the matching result for each nodule is still recommended in the
clinical setting. The importance of automated nodule matching to clinical care
and workflow remains to be determined.
Acknowledgments
We thank Thomas K. Pilgram for reviewing and commenting on the statistical
analysis method and results of our study.
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Abstract
Introduction
