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Characteristics of regions suspicious for pulmonary nodules at chest radiography
Characteristics of Regions Suspicious for Pulmonary Nodules at Chest Radiography Jeff A. Drayer, BA, Neal F. Vittitoe, MSEE, Rene Vargas-Voracek, PhD Alan H. Baydush, PhD, Carl E. Ravin, MD, Carey E. Floyd, Jr, PhD
It is not uncommon for areas suspicious for a pulmonary nodule to be noted at chest radiography. Further evaluation can be accomplished with fluoroscopy of the suspicious area to determine whether a nodule is present. The purpose of this study was to determine the characteristics of areas on chest radiographs that are suspicious for pulmonary nodules. In addition, the characteristics of areas at plain radiography that are suspicious for pulmonary nodules are compared with the characteristics of obvious nodules depicted with plain radiography. Radiologists are often forced to make difficult decisions concerning areas that are suspicious for a pulmonary nodule. A number of investigators have attempted to aid the radiologist by creating computer-aided diagnosis (CAD) systems (1-18) that can differentiate these areas into those that ultimately contain a nodule and those that do not. It would be useful, therefore, to know what characteristics of nodules are helpful in making this differentiation. A separate issue undertaken here is the comparison of areas suspicious for a pulmonary nodule and areas that
Acad Radiol 1998; 5:613-619 1From the School of Medicine (J.D,) and the Depaffment of Biomedical Engineering (N.V., C.E,F.), Duke University, Durham, NC, and the Department of Radiology, DUMC 2623, Duke University Medical Center, Durham, NC 27710 (R,V.V,, A,H.B., C.E.R., C.E,F,), Received June 26, 1997; revision requested August 27; revision received March 6, 1998; accepted April 9. Supported in part by grant number RO1CA60849 from the National Cancer Institute, Address reprint requests to C,E.F. The contents of this artiicle are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer institute. © AUR, 1998
613
Characteristics of Areas on Radiographs Suspicious for Pulmonary Nodules All Patients
Patients with Suspicious Areas
Characteristic Radius (cm) CircuLarity Compactness Contrast (OD)
Suspicious Area C o n t a i n e d Nodule (n = 80) 0.71 +-0.45 0.90 _+0.07 1.33 + 0.22 0,08 +-0,05
Suspicious Area Did Not Contain Nodule (n = 62) 0.50 + 0,27 0,89 _+0,08 1.33 + 0.30 0.05 + 0,04
i
Suspicious Area C o n t a i n e d Nodule (n = 177) 0,51 +_0,26 0.89 _+0.08 1,34 +_0,33 0.06 +-0.04
Suspicious Area Did Not Contain Nodule (n = 62) 0,49+-0.28 0.89 + 0.07 1,32 + 0,24 0.05 + 0,04 i
N o t e . - - D a t a are given as m e a n + standard deviation.
contained an obvious nodule. Certainly, radiologists are able to make this distinction. However, it would be interesting to know what characteristics of the area in question play into their decision-making process.
C a s e Selection The chest radiographs of 138 patients that contained 142 suspicious areas were examined. All patients subsequently underwent fluoroscopy to determine the presence or absence of a pulmonary nodule. The areas on the radiographs were characterized on the basis of Size, location, circularity, compactness, and contrast. Of the 142 suspicious areas, 80 proved to contain actual nodules. The chest radiographs of another 72 patients (with 97 regions, each containing an obvious nodule) who underwent computed tomography (CT) for further study (most often for surgical or fine-needle aspiration biopsy) were also examined. The same characteristics were analyzed. All radiographs were taken between 1991 and 1996 and were collected retrospectively. Each of the chest radiographs was a technically adequate posteroanterior view available for retrospective reading. Patients who underwent fluoroscopy for evaluation of possible pulmonary nodules were identified by searching the fluoroscopy log books. Patients who underwent CT for pulmonary nodules were identified in records of the tumor registry. To be included in this study, the complete radiology report for each fluoroscopy case and the pertinent CT images for those cases in which CT was performed were required. Truth File A truth file was prepared by two board-certified chest radiologists who were provided with the posteroanterior chest radiographs, pertinent CT images where applicable,
614
full radiology report, and pathology reports, when available, for each case. A laser scanner (Lumiscan model 75; Lumisys, Sunnyvale, Calif) was used to create a 2,048 x 2,048 digitized image of each radiograph. These digital images were printed to film. The radiologists then used the information provided to them to circle on this digitized reprint each area for which the patient underwent fluoroscopy or CT. In addition, the regions that contained an actual nodule were identified. The digital coordinates of these circled locations were identified and registered in a database. Nodules were defined as any lesion that represented a tumor or granuloma (calcified or noncalcified). Areas suspicious for a nipple shadow and then determined to be so by using nipple markers were not included in the study. D a t a Analysis Regions of interest (ROIs) were defined on the digital images by hand drawing an outline around the perimeter of the area on the plain radiograph that was suspicious for a nodule (or that contained an obvious nodule) by using interactive image display software. The ROIs were then analyzed for size, location, compactness, circularity, and contrast. Size was determined by counting the number of pixels in the region and then determining the radius of a circle with an area equal to the number of pixels. Location was based on placement within a 3 x 3 grid of equalsize boxes enveloping the entire lung. Circularity, a measure of an object's similarity to a circular shape, was calculated as the ratio of the number of pixels inside a circle of equal area (centered at the centroid of the ROD to the total number of pixels within the nodule outline. This measure ranges from 1 to infinity, with a perfect circle having a value of 1. The farther the value is from 1, the less circular the ROI. Compactness is another measure of similarity to circular shape. It compares the smoothness of the outline of an
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Figure 1. The range of radii for ROIsthat contained an area suspicious for a nodule,
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Figure 2. The range of circularity for ROIsthat contained an area suspicious for a nodule.
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Figure 3. The range of compactness for ROIsthat contained
an area suspicious for a nodule.
object to its area. A circular shape has the shortest perimeter for a given area, so irregular shapes of equal area will have a larger perimeter. This was measured as follows: NZ/(4n)S, where N is the number of pixels in the perimeter of the nodule and S is the number of pixels contained in the region. Contrast is the difference between the optical density within the nodule boundary and the optical density outside the nodule. In this case, the outside region was determined as being an annular region outside the nodule but within a circular region of radius 8 mm larger than that of the circle of equivalent nodular size.
ROIs were examined for the characteristics of size (radius), circularity, compactness, and contrast (Table). The first two groups included patients whose plain radiographs contained areas suspicious for a nodule and who underwent fluoroscopy for further examination: The first group ultimately proved to have nodules whereas the second group did not. In ROIs that proved to contain a nodule, the radius of the ROI ranged from 0.11 to 0.86 cm; in ROIs that proved not to contain a nodule, it ranged from 0.11 to 0.77 cm (Fig 1). Circularity ranged from 0.64 to 0.99 in ROIs that proved to contain a nodule in the suspicious area and 0.69 to 0.99 in those that proved not to contain a nodule (Fig 2). Compactness ranged from 1.14 to 2.02 in ROIs that contained a nodule and 1.14 to 2.43 in those that proved not to contain a nodule (Fig 3). Contrast ranged from -0.05 to 0.19 in ROIs that contained a nodule and -0.03 to 0.17 in those that did not (Fig 4). The other two groups examined included all patients in the study (those who underwent fluoroscopy for evaluation of suspicious areas and those who underwent CT for evaluation of obvious nodules found at plain radiography). In these two groups, the ROIs that contained areas suspicious for a nodule ranged in radius from 0.11 to 0.86 cm and in those that contained an obvious nodule from 0.10 to 1.28 cm (Fig 5). The ROIs that contained obvious nodules tended to be larger than those that were suspicious for nodules. Circularity ranged from 0.64 to 0.99 in the ROIs of areas suspicious for a nodule and 0.65 to
615
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Figure 4. The range of contrast for ROIs that contained an area suspicious for a nodule.
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radius Figure 5. The range of radii for ROIs that c o n t a i n e d either an area suspicious for a nodule or an obvious nodule.
All patients 0.99 in those that contained an obvious nodule (Fig 6). Compactness ranged from 1.14 to 2.43 in ROIs that contained areas suspicious for a nodule and 1.10 to 2.14 in those that contained an obvious nodule (Fig 7). Contrast ranged from -0.05 to 0.19 in ROIs that contained areas suspicious for a nodule and -0.05 to 0.23 in those that contained an obvious nodule (Fig 8). As is evident from Figures 1-8 and the Table, the ROIs that contained areas suspicious for a nodule were nearly identical to those that contained nodules that were obvious at plain radiography. Of the four characteristics listed in the Table, the only one in which there was any difference between groups was the radius of the ROI. However, there does appear to be a difference between the four groups in terms of the location of the ROI. As shown in Figure 9, the ROIs of areas suspicious for a nodule that did indeed contain a nodule were found more often on the right side of the lung than the left. In addition, ROIs that contained an area suspicious for a nodule but did not prove to actually contain a nodule were most often found in the upper lobe. ROIs that contained obvious nodules were not localized to any particular area in the lung. Of note are the anatomic characteristics of those areas that did not actually contain a nodule. The most common lesion in this study that was incorrectly suspected of being a nodule was a confluence of vessels (17 cases); the next most common was bone islands (12 cases). Other lesions that were originally and incorrectly suspected of being pulmonary nodules were normal rib overlap, healing rib fracture, costal cartilage calcification, and a mole.
616
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Figure6. The range of circularity for R©ls that contained either an area suspicious for a nodule or an obvious nodule.
Several investigators have described the features of pulmonary nodules. Kundel (19), in a retrospective study of nodule size, determined that 0.8-1.0 cm is the lower limit for the detection of a solitary pulmonary nodule. Kelsey et al (20) looked at nodule size and position by using superimposed masks with pseudolesions. They found that a 0.5-mm nodule was too small to detect and that nodules located in the upper lobes were easier to detect than nodules in the lower lobes. Other discussions of nodule location, such as those by Capp et al (21) and Brogdon et al (22), concentrated on the accuracy rate for
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Figure 7. The r a n g e of c o m p a c t n e s s for ROIs t h a t c o n t a i n e d either an a r e a suspicious for a n o d u l e or a n obvious nodule.
Figure 8. The r a n g e of contrast for ROIs t h a t c o n t a i n e d either an a r e a suspicious for a n o d u l e or an obvious nodule.
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Figure 9. The r a n g e of locations of ROIs t h a t c o n t a i n e d a n a r e a suspicious for a n o d u l e t h a t ultimately p r o v e d to c o n t a i n a nodule, a n a r e a suspicious for a n o d u l e t h a t ultimately p r o v e d not to c o n t a i n a nodule, or a n a r e a t h a t c o n t a i n e d an obvious nodule.
nodule detection rather than on the actual location of the nodules themselves. Carmody et al (23) investigated edge gradient through the use of masks with simulated nodules and found that nodules with sharp edges were identified faster, more frequently, and with more confidence than those with poorly defined edges. Austin et al (24) retrospectively examined radiographs in which bronchogenic
carcinoma had originally been missed and found that these nodules were detected more often in the upper than the lower lobe, were detected more often in women than in men, had poorly or moderately defined margins, and had a mean diameter of 1.6 cm _+ 0.8. To our knowledge, however, no reports have described the characteristics of areas that are suspicious but not definitive for the pres-
617
ence of pulmonary nodules. The goal of many radiologic researchers over the past several decades has been to find a way to make pulmonary nodules more easily detectable at chest radiography. This could not only decrease the number of patients who undergo fluoroscopy but increase the number of cases of lung cancer that are detected early enough for treatment to be effective. One way to do this is through the creation of CAD systems. These systems are algorithms that, based on information about the characteristics of nodules, are designed to find those regions that are most likely to contain nodules and point them out to the radiologist. CAD systems can approach a radiograph in several ways. Some are designed to detect possible nodules (114), while others attempt to classify areas that are considered by an observer to be suspicious for a nodule as containing an actual nodule or not (3-6). It is important, then, to know the characteristics with which areas suspicious for pulmonary nodules that actually contain nodules can be differentiated from those that do not, since this is one of the areas in which radiologists could actually benefit from computer aid. In the present study, two sets of comparisons of size, circularity, compactness, contrast, and location were made. The first was between areas at plain radiography that were suspicious for a nodule and ultimately proved actually to contain a nodule and areas at plain radiography that were suspicious for a nodule but proved not to contain a nodule. In this comparison, the above characteristics as we have applied them, and as others have used them, did not distinguish between areas that ultimately contained nodules and those that did not. It is interesting to note that circularity and contrast are two parameters that many CAD systems include to distinguish nodules from other types of lesions. Our work would suggest, then, that while these features may be important to reduce false identification of obviously nonnodular opacities in CAD systems, they are unlikely to be useful in making a distinction in subtle cases. While conspicuity has been discussed as an indicator of detectability, we found that the calculation was extremely sensitive to the location of the ROI and displayed poor repeatability. The second comparison was between areas at plain radiography that were suspicious for a nodule and areas at plain radiography that contained an obvious nodule. Here, too, there were no differences other than a difference in location and a possible small difference in size. This is striking, since these two groups are being differentiated by radiologists somehow. This differentiation,
618
however, is not being made by using the parameters studied here. Therefore, there must be a way to distinguish between these two groups, although we have not yet determined how. This study, though, would suggest that there are characteristics other than those described here that differ between the two groups. It is also possible that a certain combination of characteristics and not any individual characteristic can be used to differentiate between the groups. A possible source of error in this study is in the handdrawn outlines of the ROIs. These cases were chosen based on the difficulty in detecting the nodule, and the regions were often extremely hard to distinguish from background objects or noise. To partially overcome this difficulty, regions were enlarged on the computer screen and the window and level were manipulated by the experimenter until the best distinction between the ROI and the background was obtained. In addition, radiology and pathology reports were used, when applicable, to get an estimate of the region's size. This study describes and shows the range of values in size, circularity, compactness, contrast, and location that can be expected in the clinical environment for areas seen at plain chest radiography that are suspicious for the presence of a pulmonary nodule that actually contain a nodule and those that do not. It also gives this same information for areas seen at plain radiography that contain an obvious nodule. For the future development of CAD systems, it is important to identify radiographic characteristics that distinguish nodules from other types of lesions in subtle cases. The results of this study indicate that size, circularity, compactness, contrast, and location are not sufficient to perform this task and that more complex characteristics, such as texture or anatomic context, may be required. !EFERENCE~ 1. Ballard DH, Sklansky J. A ladder-structured decision tree for recognizing tumors in chest radiography, IEEETrans Comput 1976; C-25:503-513, 2. Garg S, Floyd CE, Ravin CE. Artificial neural network for pulmonary nodule detection: preliminary human observer comparison, Proc SPIE 1994; 2167:623-629, 3. Giger ML, Ahn N, Doi K, MacMahon H, Metz CE, Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections, Med Phys 1990; 17:861-865. 4. Giger ML, boi K, MacMahon H. Image feature analysis and computer-aided diagnosis in digital radiography. III, Automated detection of nodules in peripheral lung fields. Med Phys 1988; 15:158-166, 5. Giger ML, Doi K, MacMahon H, Metz C E, Yin FF, Pulmonary nodules: computer-aided detection in digital chest images, RadioGraphics 1990; 10:41-51.
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6. Lampeter WA, Wandtke JC. Computerized search of chest radiographs for nodules. Invest Radio11986; 21:384-390. 7, Lin JS, Lo SC, Hasegawa A, Freedman MT. Reduction of false positives in !ung nodule detection using a two-level neural classification, IEEETrans Med Imaging 1996; 15:206-217, 8. Lo SC, Chan HP, Lin JS, Li H, Freedman MT, Mun SK, Artificial convolution neural network for medical image pattern recognition, Neural Networks 1995; 8:1201-1204. 9. Lo SC, Freedman MT, Lin JS, Mun SK, Automatic lung nodule detection using profile matching and back-propagation neural network techniques, J Digit Imaging 1993; 6:48-54, 10, Lo SC, Lou SA, Lin JS, Freedman MT, Chien MV, Mun SK. Artificial convolution neural network techniques and applications for lung nodule detection, IEEETrans Med Imaging 1995; 14:711-718. 11. Matsumoto T, Yoshimura H, Doi K, et al. Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules. Invest Radio11992; 27:587-597. 12. Wu YC, Doi K, Giger ML. Detection of lung nodules in digital chest radiographs using artificial neural networks: a pilot study. J Digit Imaging 1995; 8:88-94, 13, Wu YC, Doi K, Giger ML, Metz CE, Zhang W. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme, J Digit Imaging 1994; 7:196-207, 14, Yoshimura H, Giger ML, Doi K, MacMahon H, Montner SM, Computerized scheme for the detection of pulmonary nodules: a nonlinear filtering technique. Invest Radio11992; 27:124-129,
REGIONS SUSPICIOUS FOR P U L M O N A R Y NODULES
15. Lin JS, Hasegawa A, Freedman MT, Mun SK. Differentiation between nodules and end-on vessels using a convolution neural network architecture, J Digit Imaging 1995; 8:132-141. 16. Lin JS, Lo SC, Freedman MT, Mun SK. Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiographs, Proc SPIE 1995; 2434:563-570, 17, Sherrier RH, Chiles C, Johnson GA, Ravin CE. Differentiation of benign from malignant pulmonary nodules with digitized chest radiographs. Radiology 1987; 162:645-649. 18, Vittitoe N, Floyd C Jr, Characterization of pulmonary nodules by means of ffactal dimension (abstr). Radiology 1995; 197(P):293, 19. Kundel HL. Predictive value and threshold detectability of lung tumors. Radiology 1981; 139:25-29. 20. Kelsey CA, Moseley RD, Brogdon DG, Bhave DG, Hallberg J. Effect of size and position on chest lesion detection. A JR 1977; 129:205-208. 21. Capp MP, Gray J, Seeley G. Psychophysics from a radiologist's point of view. Proc SPIE 1974; 46:142-147, 22. Brogden BG, Kelsey CA, Moseley RDJ. Factors affecting perception of pulmonary lesions. Radiol Clin North Am 1983; 21:633-654. 23. Carmody DP, Nodine CF, Kundel HL. Global and segmented search for lung nodules of different edge gradients. Invest Radiol 1980; 15:224-33. 24, Austin JHM, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect, Radiology 1992; 182:115-122.
619
It is not uncommon for areas suspicious for a pulmonary nodule to be noted at chest radiography. Further evaluation can be accomplished with fluoroscopy of the suspicious area to determine whether a nodule is present. The purpose of this study was to determine the characteristics of areas on chest radiographs that are suspicious for pulmonary nodules. In addition, the characteristics of areas at plain radiography that are suspicious for pulmonary nodules are compared with the characteristics of obvious nodules depicted with plain radiography. Radiologists are often forced to make difficult decisions concerning areas that are suspicious for a pulmonary nodule. A number of investigators have attempted to aid the radiologist by creating computer-aided diagnosis (CAD) systems (1-18) that can differentiate these areas into those that ultimately contain a nodule and those that do not. It would be useful, therefore, to know what characteristics of nodules are helpful in making this differentiation. A separate issue undertaken here is the comparison of areas suspicious for a pulmonary nodule and areas that
Acad Radiol 1998; 5:613-619 1From the School of Medicine (J.D,) and the Depaffment of Biomedical Engineering (N.V., C.E,F.), Duke University, Durham, NC, and the Department of Radiology, DUMC 2623, Duke University Medical Center, Durham, NC 27710 (R,V.V,, A,H.B., C.E.R., C.E,F,), Received June 26, 1997; revision requested August 27; revision received March 6, 1998; accepted April 9. Supported in part by grant number RO1CA60849 from the National Cancer Institute, Address reprint requests to C,E.F. The contents of this artiicle are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer institute. © AUR, 1998
613
Characteristics of Areas on Radiographs Suspicious for Pulmonary Nodules All Patients
Patients with Suspicious Areas
Characteristic Radius (cm) CircuLarity Compactness Contrast (OD)
Suspicious Area C o n t a i n e d Nodule (n = 80) 0.71 +-0.45 0.90 _+0.07 1.33 + 0.22 0,08 +-0,05
Suspicious Area Did Not Contain Nodule (n = 62) 0.50 + 0,27 0,89 _+0,08 1.33 + 0.30 0.05 + 0,04
i
Suspicious Area C o n t a i n e d Nodule (n = 177) 0,51 +_0,26 0.89 _+0.08 1,34 +_0,33 0.06 +-0.04
Suspicious Area Did Not Contain Nodule (n = 62) 0,49+-0.28 0.89 + 0.07 1,32 + 0,24 0.05 + 0,04 i
N o t e . - - D a t a are given as m e a n + standard deviation.
contained an obvious nodule. Certainly, radiologists are able to make this distinction. However, it would be interesting to know what characteristics of the area in question play into their decision-making process.
C a s e Selection The chest radiographs of 138 patients that contained 142 suspicious areas were examined. All patients subsequently underwent fluoroscopy to determine the presence or absence of a pulmonary nodule. The areas on the radiographs were characterized on the basis of Size, location, circularity, compactness, and contrast. Of the 142 suspicious areas, 80 proved to contain actual nodules. The chest radiographs of another 72 patients (with 97 regions, each containing an obvious nodule) who underwent computed tomography (CT) for further study (most often for surgical or fine-needle aspiration biopsy) were also examined. The same characteristics were analyzed. All radiographs were taken between 1991 and 1996 and were collected retrospectively. Each of the chest radiographs was a technically adequate posteroanterior view available for retrospective reading. Patients who underwent fluoroscopy for evaluation of possible pulmonary nodules were identified by searching the fluoroscopy log books. Patients who underwent CT for pulmonary nodules were identified in records of the tumor registry. To be included in this study, the complete radiology report for each fluoroscopy case and the pertinent CT images for those cases in which CT was performed were required. Truth File A truth file was prepared by two board-certified chest radiologists who were provided with the posteroanterior chest radiographs, pertinent CT images where applicable,
614
full radiology report, and pathology reports, when available, for each case. A laser scanner (Lumiscan model 75; Lumisys, Sunnyvale, Calif) was used to create a 2,048 x 2,048 digitized image of each radiograph. These digital images were printed to film. The radiologists then used the information provided to them to circle on this digitized reprint each area for which the patient underwent fluoroscopy or CT. In addition, the regions that contained an actual nodule were identified. The digital coordinates of these circled locations were identified and registered in a database. Nodules were defined as any lesion that represented a tumor or granuloma (calcified or noncalcified). Areas suspicious for a nipple shadow and then determined to be so by using nipple markers were not included in the study. D a t a Analysis Regions of interest (ROIs) were defined on the digital images by hand drawing an outline around the perimeter of the area on the plain radiograph that was suspicious for a nodule (or that contained an obvious nodule) by using interactive image display software. The ROIs were then analyzed for size, location, compactness, circularity, and contrast. Size was determined by counting the number of pixels in the region and then determining the radius of a circle with an area equal to the number of pixels. Location was based on placement within a 3 x 3 grid of equalsize boxes enveloping the entire lung. Circularity, a measure of an object's similarity to a circular shape, was calculated as the ratio of the number of pixels inside a circle of equal area (centered at the centroid of the ROD to the total number of pixels within the nodule outline. This measure ranges from 1 to infinity, with a perfect circle having a value of 1. The farther the value is from 1, the less circular the ROI. Compactness is another measure of similarity to circular shape. It compares the smoothness of the outline of an
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Figure 1. The range of radii for ROIsthat contained an area suspicious for a nodule,
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Figure 2. The range of circularity for ROIsthat contained an area suspicious for a nodule.
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Figure 3. The range of compactness for ROIsthat contained
an area suspicious for a nodule.
object to its area. A circular shape has the shortest perimeter for a given area, so irregular shapes of equal area will have a larger perimeter. This was measured as follows: NZ/(4n)S, where N is the number of pixels in the perimeter of the nodule and S is the number of pixels contained in the region. Contrast is the difference between the optical density within the nodule boundary and the optical density outside the nodule. In this case, the outside region was determined as being an annular region outside the nodule but within a circular region of radius 8 mm larger than that of the circle of equivalent nodular size.
ROIs were examined for the characteristics of size (radius), circularity, compactness, and contrast (Table). The first two groups included patients whose plain radiographs contained areas suspicious for a nodule and who underwent fluoroscopy for further examination: The first group ultimately proved to have nodules whereas the second group did not. In ROIs that proved to contain a nodule, the radius of the ROI ranged from 0.11 to 0.86 cm; in ROIs that proved not to contain a nodule, it ranged from 0.11 to 0.77 cm (Fig 1). Circularity ranged from 0.64 to 0.99 in ROIs that proved to contain a nodule in the suspicious area and 0.69 to 0.99 in those that proved not to contain a nodule (Fig 2). Compactness ranged from 1.14 to 2.02 in ROIs that contained a nodule and 1.14 to 2.43 in those that proved not to contain a nodule (Fig 3). Contrast ranged from -0.05 to 0.19 in ROIs that contained a nodule and -0.03 to 0.17 in those that did not (Fig 4). The other two groups examined included all patients in the study (those who underwent fluoroscopy for evaluation of suspicious areas and those who underwent CT for evaluation of obvious nodules found at plain radiography). In these two groups, the ROIs that contained areas suspicious for a nodule ranged in radius from 0.11 to 0.86 cm and in those that contained an obvious nodule from 0.10 to 1.28 cm (Fig 5). The ROIs that contained obvious nodules tended to be larger than those that were suspicious for nodules. Circularity ranged from 0.64 to 0.99 in the ROIs of areas suspicious for a nodule and 0.65 to
615
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All patients 0.99 in those that contained an obvious nodule (Fig 6). Compactness ranged from 1.14 to 2.43 in ROIs that contained areas suspicious for a nodule and 1.10 to 2.14 in those that contained an obvious nodule (Fig 7). Contrast ranged from -0.05 to 0.19 in ROIs that contained areas suspicious for a nodule and -0.05 to 0.23 in those that contained an obvious nodule (Fig 8). As is evident from Figures 1-8 and the Table, the ROIs that contained areas suspicious for a nodule were nearly identical to those that contained nodules that were obvious at plain radiography. Of the four characteristics listed in the Table, the only one in which there was any difference between groups was the radius of the ROI. However, there does appear to be a difference between the four groups in terms of the location of the ROI. As shown in Figure 9, the ROIs of areas suspicious for a nodule that did indeed contain a nodule were found more often on the right side of the lung than the left. In addition, ROIs that contained an area suspicious for a nodule but did not prove to actually contain a nodule were most often found in the upper lobe. ROIs that contained obvious nodules were not localized to any particular area in the lung. Of note are the anatomic characteristics of those areas that did not actually contain a nodule. The most common lesion in this study that was incorrectly suspected of being a nodule was a confluence of vessels (17 cases); the next most common was bone islands (12 cases). Other lesions that were originally and incorrectly suspected of being pulmonary nodules were normal rib overlap, healing rib fracture, costal cartilage calcification, and a mole.
616
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Several investigators have described the features of pulmonary nodules. Kundel (19), in a retrospective study of nodule size, determined that 0.8-1.0 cm is the lower limit for the detection of a solitary pulmonary nodule. Kelsey et al (20) looked at nodule size and position by using superimposed masks with pseudolesions. They found that a 0.5-mm nodule was too small to detect and that nodules located in the upper lobes were easier to detect than nodules in the lower lobes. Other discussions of nodule location, such as those by Capp et al (21) and Brogdon et al (22), concentrated on the accuracy rate for
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Figure 7. The r a n g e of c o m p a c t n e s s for ROIs t h a t c o n t a i n e d either an a r e a suspicious for a n o d u l e or a n obvious nodule.
Figure 8. The r a n g e of contrast for ROIs t h a t c o n t a i n e d either an a r e a suspicious for a n o d u l e or an obvious nodule.
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Figure 9. The r a n g e of locations of ROIs t h a t c o n t a i n e d a n a r e a suspicious for a n o d u l e t h a t ultimately p r o v e d to c o n t a i n a nodule, a n a r e a suspicious for a n o d u l e t h a t ultimately p r o v e d not to c o n t a i n a nodule, or a n a r e a t h a t c o n t a i n e d an obvious nodule.
nodule detection rather than on the actual location of the nodules themselves. Carmody et al (23) investigated edge gradient through the use of masks with simulated nodules and found that nodules with sharp edges were identified faster, more frequently, and with more confidence than those with poorly defined edges. Austin et al (24) retrospectively examined radiographs in which bronchogenic
carcinoma had originally been missed and found that these nodules were detected more often in the upper than the lower lobe, were detected more often in women than in men, had poorly or moderately defined margins, and had a mean diameter of 1.6 cm _+ 0.8. To our knowledge, however, no reports have described the characteristics of areas that are suspicious but not definitive for the pres-
617
ence of pulmonary nodules. The goal of many radiologic researchers over the past several decades has been to find a way to make pulmonary nodules more easily detectable at chest radiography. This could not only decrease the number of patients who undergo fluoroscopy but increase the number of cases of lung cancer that are detected early enough for treatment to be effective. One way to do this is through the creation of CAD systems. These systems are algorithms that, based on information about the characteristics of nodules, are designed to find those regions that are most likely to contain nodules and point them out to the radiologist. CAD systems can approach a radiograph in several ways. Some are designed to detect possible nodules (114), while others attempt to classify areas that are considered by an observer to be suspicious for a nodule as containing an actual nodule or not (3-6). It is important, then, to know the characteristics with which areas suspicious for pulmonary nodules that actually contain nodules can be differentiated from those that do not, since this is one of the areas in which radiologists could actually benefit from computer aid. In the present study, two sets of comparisons of size, circularity, compactness, contrast, and location were made. The first was between areas at plain radiography that were suspicious for a nodule and ultimately proved actually to contain a nodule and areas at plain radiography that were suspicious for a nodule but proved not to contain a nodule. In this comparison, the above characteristics as we have applied them, and as others have used them, did not distinguish between areas that ultimately contained nodules and those that did not. It is interesting to note that circularity and contrast are two parameters that many CAD systems include to distinguish nodules from other types of lesions. Our work would suggest, then, that while these features may be important to reduce false identification of obviously nonnodular opacities in CAD systems, they are unlikely to be useful in making a distinction in subtle cases. While conspicuity has been discussed as an indicator of detectability, we found that the calculation was extremely sensitive to the location of the ROI and displayed poor repeatability. The second comparison was between areas at plain radiography that were suspicious for a nodule and areas at plain radiography that contained an obvious nodule. Here, too, there were no differences other than a difference in location and a possible small difference in size. This is striking, since these two groups are being differentiated by radiologists somehow. This differentiation,
618
however, is not being made by using the parameters studied here. Therefore, there must be a way to distinguish between these two groups, although we have not yet determined how. This study, though, would suggest that there are characteristics other than those described here that differ between the two groups. It is also possible that a certain combination of characteristics and not any individual characteristic can be used to differentiate between the groups. A possible source of error in this study is in the handdrawn outlines of the ROIs. These cases were chosen based on the difficulty in detecting the nodule, and the regions were often extremely hard to distinguish from background objects or noise. To partially overcome this difficulty, regions were enlarged on the computer screen and the window and level were manipulated by the experimenter until the best distinction between the ROI and the background was obtained. In addition, radiology and pathology reports were used, when applicable, to get an estimate of the region's size. This study describes and shows the range of values in size, circularity, compactness, contrast, and location that can be expected in the clinical environment for areas seen at plain chest radiography that are suspicious for the presence of a pulmonary nodule that actually contain a nodule and those that do not. It also gives this same information for areas seen at plain radiography that contain an obvious nodule. For the future development of CAD systems, it is important to identify radiographic characteristics that distinguish nodules from other types of lesions in subtle cases. The results of this study indicate that size, circularity, compactness, contrast, and location are not sufficient to perform this task and that more complex characteristics, such as texture or anatomic context, may be required. !EFERENCE~ 1. Ballard DH, Sklansky J. A ladder-structured decision tree for recognizing tumors in chest radiography, IEEETrans Comput 1976; C-25:503-513, 2. Garg S, Floyd CE, Ravin CE. Artificial neural network for pulmonary nodule detection: preliminary human observer comparison, Proc SPIE 1994; 2167:623-629, 3. Giger ML, Ahn N, Doi K, MacMahon H, Metz CE, Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections, Med Phys 1990; 17:861-865. 4. Giger ML, boi K, MacMahon H. Image feature analysis and computer-aided diagnosis in digital radiography. III, Automated detection of nodules in peripheral lung fields. Med Phys 1988; 15:158-166, 5. Giger ML, Doi K, MacMahon H, Metz C E, Yin FF, Pulmonary nodules: computer-aided detection in digital chest images, RadioGraphics 1990; 10:41-51.
Vol 5, No 9, S e p t e m b e r 1998
6. Lampeter WA, Wandtke JC. Computerized search of chest radiographs for nodules. Invest Radio11986; 21:384-390. 7, Lin JS, Lo SC, Hasegawa A, Freedman MT. Reduction of false positives in !ung nodule detection using a two-level neural classification, IEEETrans Med Imaging 1996; 15:206-217, 8. Lo SC, Chan HP, Lin JS, Li H, Freedman MT, Mun SK, Artificial convolution neural network for medical image pattern recognition, Neural Networks 1995; 8:1201-1204. 9. Lo SC, Freedman MT, Lin JS, Mun SK, Automatic lung nodule detection using profile matching and back-propagation neural network techniques, J Digit Imaging 1993; 6:48-54, 10, Lo SC, Lou SA, Lin JS, Freedman MT, Chien MV, Mun SK. Artificial convolution neural network techniques and applications for lung nodule detection, IEEETrans Med Imaging 1995; 14:711-718. 11. Matsumoto T, Yoshimura H, Doi K, et al. Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules. Invest Radio11992; 27:587-597. 12. Wu YC, Doi K, Giger ML. Detection of lung nodules in digital chest radiographs using artificial neural networks: a pilot study. J Digit Imaging 1995; 8:88-94, 13, Wu YC, Doi K, Giger ML, Metz CE, Zhang W. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme, J Digit Imaging 1994; 7:196-207, 14, Yoshimura H, Giger ML, Doi K, MacMahon H, Montner SM, Computerized scheme for the detection of pulmonary nodules: a nonlinear filtering technique. Invest Radio11992; 27:124-129,
REGIONS SUSPICIOUS FOR P U L M O N A R Y NODULES
15. Lin JS, Hasegawa A, Freedman MT, Mun SK. Differentiation between nodules and end-on vessels using a convolution neural network architecture, J Digit Imaging 1995; 8:132-141. 16. Lin JS, Lo SC, Freedman MT, Mun SK. Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiographs, Proc SPIE 1995; 2434:563-570, 17, Sherrier RH, Chiles C, Johnson GA, Ravin CE. Differentiation of benign from malignant pulmonary nodules with digitized chest radiographs. Radiology 1987; 162:645-649. 18, Vittitoe N, Floyd C Jr, Characterization of pulmonary nodules by means of ffactal dimension (abstr). Radiology 1995; 197(P):293, 19. Kundel HL. Predictive value and threshold detectability of lung tumors. Radiology 1981; 139:25-29. 20. Kelsey CA, Moseley RD, Brogdon DG, Bhave DG, Hallberg J. Effect of size and position on chest lesion detection. A JR 1977; 129:205-208. 21. Capp MP, Gray J, Seeley G. Psychophysics from a radiologist's point of view. Proc SPIE 1974; 46:142-147, 22. Brogden BG, Kelsey CA, Moseley RDJ. Factors affecting perception of pulmonary lesions. Radiol Clin North Am 1983; 21:633-654. 23. Carmody DP, Nodine CF, Kundel HL. Global and segmented search for lung nodules of different edge gradients. Invest Radiol 1980; 15:224-33. 24, Austin JHM, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect, Radiology 1992; 182:115-122.
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