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Original Research |
1 Department of Radiology, Vancouver General Hospital, Vancouver, British
Columbia, V5Z 1M9 Canada.
2 Department of Radiology, St. Paul's Hospital, 1081 Burrard St., Vancouver,
British Columbia, V6Z 1Y6 Canada.
Received December 27, 2004;
accepted after revision March 14, 2005.
Address correspondence to P. L. Cooperberg.
Abstract
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MATERIALS AND METHODS. One hundred fifty consecutive low-dose screening CT examinations were independently evaluated by a radiologist and a CAD pulmonary nodule detection system (R2 Technology) designed to identify nodules larger than 4 mm in maximum long-axis diameter. All discrepancies between the two techniques were reviewed by one of another two radiologists working in consensus with the initial interpreting radiologist, and a "true" nodule count was determined. Detected nodules were classified by size, density, and location. The performance of the initial radiologist and the CAD system were compared.
RESULTS. The radiologist detected 518 nodules and the CAD system, 934 nodules. Of the 1,106 separate nodules detected using the two techniques, 628 were classified as true nodules on consensus review. Of the true nodules present, the radiologist detected 518 (82%) of 628 nodules and the CAD, 456 (73%) of 628 nodules. All 518 radiologist-detected nodules were true nodules, and 456 (49%) of 934 of CAD-detected nodules were true nodules. The radiologist missed 110 true nodules that were only detected by CAD. In six patients, these were the only nodules detected in the examination, changing the imaging follow-up protocol. CAD identified 478 lesions that on consensus review were false-positive nodules, a rate of 3.19 (478/150) per patient.
CONCLUSION. CAD detected 72.6% of true nodules and detected nodules in six (4%) patients not identified by radiologists, changing the imaging follow-up protocol of these subjects. In this study, the combined review of low-dose CT scans by both the radiologist and CAD was necessary to identify all nodules.
Keywords: computer-aided detection • CT • low-dose CT • lung cancer • pulmonary nodules
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The original axial images with a 1.25-mm slice were used for the CAD system. Images were reconstructed with 2.5-mm slice thickness for radiologist evaluation. These images were displayed on a conventional Windows-based (Microsoft) workstation with commercially available viewing software (eFilm, Merge Technologies) and viewed by a radiologist. The average number of slices per patient at 1.25 mm was 404 (range, 194-525 slices).
Automated Nodule Detection Method
The CAD system consists of two parts: a CAD server and a CAD workstation.
CT data are transferred from the CT scanner to the CAD server over the network
using the DICOM protocol. The CAD server accepts and analyzes the scans by
segmenting the lung parenchyma from the vessels, mediastinum, and chest wall.
Other bridging techniques are used to include lesions that may be touching the
chest wall.
The CAD workstation is user controlled. The findings of CAD are presented in three windows on the monitor: the original 2D axial images; a lung nodule map (an anteroposterior projection such as a chest X-ray), and a 3D rendering image (Fig. 1).
Suspected lesions are circled in green on the nodule map. When a green-circled "nodule" is clicked with the mouse, the appropriate axial slice will appear, with the suspected nodule encircled in green. Also, the appropriate region on the 3D window appears with the suspected nodule colored green. The 3D image can be rotated and otherwise modified for the radiologist to decide if it is a true nodule or not. The size, volume, and density of the nodule are displayed on the left.
A nodule can be added by the radiologist and it will be surrounded by a green hexagon. The size, volume, and density are then also determined by the CAD system.
Radiologist and CAD Performance
First, all CT examinations were interpreted by a radiology fellow
experienced in detection of pulmonary nodules, using a PACS workstation
(eFilm, Merge Technologies) with a 2.5-mm slice thickness. Then the CT was
processed by the CAD system. The results of the two techniques were compared,
and a final decision was made. If there was a discrepancy between the
radiologist and the CAD system, consensus was made with another of the two
radiologists. The suspected nodules detected by CAD were divided into four
groups: both-positive (BP) referred to the true nodules detected by both CAD
and the radiologist, true-positive (TP) referred to the true nodules detected
by CAD but missed by the radiologist, false-negative (FN) referred to the true
nodules detected by the radiologist but missed by CAD, and false-positive (FP)
referred to the structures detected by CAD as a "nodule" but
rejected by the radiologists.
The location of the true nodules was classified as follows [7]. A subpleural nodule had pleural contact. A peripheral nodule was within 2 cm of, but not touching, the pleura. A hilar nodule was within 2 cm of the hilum. A central nodule was situated between the peripheral and hilar zones.
The nodules were separated into the following three groups by diameter: less than 4 mm, greater than or equal to 4 mm but smaller than or equal to 10 mm, and greater than 10 mm.
We also classified them into "solid" pulmonary nodules and "nonsolid" pulmonary nodules using the peak H of -100 as described by Miller D et al. (presented at the 2003 annual meeting of the Radiological Society of North America).
The performance of the CAD system was evaluated in terms of nodule detected (especially additional nodules detected) and the number of false-positives per CT study. The reasons for CAD false-negatives and false-positives were analyzed.
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Of the 115 small nodules (< 4 mm) missed by CAD (Table 1), besides the size algorithm limitation, lower density and contact to normal structures (e.g., pleura and vessel) further decreased the detection performance. Thirty-seven nodules had a peak attenuation value of less than -100 H; 34 were in contact with the pleura (Fig. 3A) or vessel (Fig. 3B).
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Fifty moderate nodules (4 mm 
diameter 10 mm) were missed by CAD.
Of these, six subpleural nodules were missed because they had pleural contact
(Fig. 4A). Among 44 of 50
central and peripheral nodules without obvious pleural contact, 21 had lower
density (CTpeak < -100 H)
(Fig. 4B); 11 nodules were
attached to a linear pleural tag or the normal intrapulmonary structures, such
as fissure (Fig. 4C) and vessel
(Fig. 4D) and thereby were
excluded by the segmentation algorithm. There was no explanation for the other
12 nodules that were missed (Fig.
4E).
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Of the 22 central nodules missed by CAD, six were less than 4 mm. Two of the six were attached to normal structures (Fig. 3B); the other four had low density (CTpeak < -100 H), which made them more difficult for CAD to identify. With the other 16 central nodules greater than 4 mm, CAD failed to detect them, possibly because of their lower density (n = 8; CTpeak < -100 H), or because they were abutting normal structures (n = 6) and were thereby excluded by the segmentation algorithm. No explanation was determined for the remaining two nodules.
Thirty-four subpleural nodules were missed by CAD presumably because of the segmentation algorithm. In addition, 27 of them were smaller than 4 mm (Fig. 3A). There were 116 peripheral nodules missed by CAD; 82 (71%) of 116 were smaller than 4 mm (moreover, 31 of 82 had a peak attenuation value < -100 H). Among the remaining 34 (29%) nodules greater than 4 mm, a CTpeak of less than -100 H was seen in 13 nodules (Fig. 4B). Attachment to the pleura (Fig. 5), vessel (Fig. 4D), and fissure (Fig. 4C) was seen in another 13 nodules; no obvious reason was found in the remaining eight nodules (Fig. 4E).
A CTpeak greater than or equal to -100 H was seen in 561 (89.3%) of 628 true pulmonary nodules, which is considered to represent a solid pulmonary nodule (Miller D et al., presented at the 2003 annual meeting of the Radiological Society of North America). The detection sensitivity of solid pulmonary nodules was 80% (448 of 561 nodules) with CAD and 81% for the radiologist (455 of 561 nodules). These are very close. Solid nodules were found only by CAD and missed by the radiologist in 106 (18.9%) of 561 nodules (Figs. 2A, 2B, and 2C). CAD missed 113 (20.1%) of 561 solid nodules. Seventy-eight (70%) of those were less than 4 mm (Fig. 3A). Among the remaining 35 (30%) of 113 nodules greater than or equal to 4 mm, attachment to the pleura (Figs. 4A and 5) or fissure (Fig. 4C) and vessels (Fig. 4D) was seen in 23 of 35; no obvious reason was found in the remaining 12 (Fig. 4E).
Reasons for the 478 false-positives from CAD and thus defined as false-positive (FP) include vessel, 262 (55%); pleural, 114 (24%); scars, 57 (12%); and others (e.g., bone, ground glass, and chest wall soft tissue), 45 (9%) (Figs. 7A and 7B).
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A wide variation exists in the detection sensitivity by CAD of pulmonary nodules in previously published studies [1, 7, 8, 10]. The detection sensitivity was 84% in a study by Armato et al. [1], who used CAD retrospectively in 31 patients with missed lung cancer. In Brown et al. [8], CAD detected 74% of nodules in 15 patients who had lung cancer. In a study by Wormanns et al. [7], only 38% of nodules were detected in 85 healthy subjects. Goo et al. [10] studied 50 volunteers and found a sensitivity of 65%. In our study, CAD had a 73% sensitivity in 150 patients.
False-positive rates range from three to 13 per CT study [1, 7, 8, 10]. In our study, there were 3.19 false-positives per study.
In our study, there was a high prevalence of nodules (mean, 4.18 per patient). This is because most cases were pulmonary nodule follow-ups of the original screening examinations performed in outside facilities, so very few cases had no nodules. Only 16 of the total 150 patients had no nodules detected, which might be a selection bias. Despite that, there were six patients whose nodules were only detected by CAD. In a screening situation, these patients would have been lost to follow-up, possibly missing cancers.
The high false-negative rate of CAD limits its application as a stand-alone
technique. Missing true nodules by CAD in our study was predominately due to
size limitation (
4 mm), attenuation limitation (CT peak -100 H), and
the segmentation algorithm (CAD only recognizes nodules entirely surrounded by
lung parenchyma). Our results showed that these three factors are interactive
in influencing the CAD detection performance. However, this CAD system still
picked up 62 of the total 291 small nodules (< 4 mm)
(Table 1). The reason is that
although the current CAD algorithm targets nodules with diameters greater than
4 mm and less than 30 mm (in which a nodule is more typically described as a
mass), the CAD algorithm also examines the nodular findings with diameters
between 2 and 4 mm. For these smaller nodular findings, a stricter set of
criteria on shape (i.e., more spherical) and location (i.e., clearly not part
of adjacent structures) are applied to determine whether they are presented as
CAD findings. Further development of the technology hopefully will overcome
these deficits. Our results also showed that CAD and the radiologist worked in
a complementary fashion in different lung zones because neither of them was
able to find every nodule (Fig.
6). The radiologist has little difficulty in finding the
peripheral and subpleural nodules even if they are small because there are no
vessels of similar size near the pleural surface (Figs.
3A,
4A,
4B,
4C,
4D, and
4E). CAD is more sensitive in
showing central nodules (Figs.
2A,
2B, and
2C), especially hilar nodules
among the large vessels, which are prone to be misinterpreted as vessels and
overlooked by the radiologist.
The high false-positive rate of CAD requires the radiologists to look at each suspected "nodule" to confirm its authenticity. In our study, the false-positive nodules were seen in 122 of the total 150 patients, which contribute to 3.19 false-positive rate per study. Although the analysis of nodules using one-way system (either by a CAD system or a radiologist) was satisfactory in 56 (37.3%) patients of our study, radiologists still needed to look at 46 of 56 CT scans to reject the false-positive. The causes of false-positives are vessel (54%), pleura (24%), and scar (12%). The others (10%) include consolidation, bone structure, and soft tissue of the chest wall. This has been described in other studies [1, 7, 8, 10, 12]. The particular software of our CAD system facilitates differentiating false-positives by the 3D-rendering image. The capability to rotate this image facilitates the distinction of a true nodule from the pulmonary vascular tree or pleural thickening. Although the false-positive rate is a drawback, CAD should have a high sensitivity even at the expense of a low specificity.
The main characteristic to diagnose malignant nodules is their growth over time [13-18]. Algorithms are already being developed to do temporal comparisons on follow-up studies. All previously detected nodules would be automatically assessed to see if they have increased in volume and at what rate. This feature probably will greatly enhance the diagnostic value of CAD systems in CT screening for early lung cancer [19].
CAD software is useful to supplement radiologists' detection performance. However, at present, it is not adequate as a stand-alone procedure. Furthermore, all suspected lesions detected by CAD must be interpreted by radiologists to rule out false-positives. In the future, temporal comparison should further improve the usefulness of CAD in the early detection of lung cancer.
Acknowledgments
We wish to express appreciation to John Mayo for help in reviewing the
manuscript.
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