- Email: [email protected]
The utility of computer-aided detection (CAD) for lung cancer screening using low-dose CT
International Congress Series 1281 (2005) 1137 – 1142
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The utility of computer-aided detection (CAD) for lung cancer screening using low-dose CT Heidi C. RobertsT, Demetris Patsios, Michael Kucharczyk, Narinder Paul, Timothy P. Roberts Department of Medical Imaging, University Health Network, Toronto, ON, Canada
Abstract. Multi-detector CT scanners facilitate the detection of very small nodules within the lung parenchyma, with a sensitivity that is significantly superior to that of chest radiography. It has been demonstrated that an unacceptable number of nodules may be missed by radiologist reading. The issues of reader fatigue and the presentation of essentially redundant image appearances are addressed with the development of semi-automated computer-aided detection (CAD) software algorithms. The purpose of this study is to assess one of the commercially available tools, the ImageChecker-CT, from R2 Technology, within a state-of-the art screening study. D 2005 CARS & Elsevier B.V. All rights reserved. Keywords: Lung cancer screening; CAD; Low-dose CT
1. Introduction Lung cancer screening using low-dose computed tomography has gained rising recognition in the last decade and–even though its utility remains heavily discussed [1–3]– is increasingly performed to detect low grade lung cancers in high-risk smokers [4–7]. Modern, state-of-the art technology utilizes multi-detector CT scanners, yielding thin (1– 1.5 mm) slices covering the entire chest. This protocol facilitates the detection of very small nodules within the lung parenchyma, with a sensitivity that is significantly superior to that of chest radiography [1,8]. However, it does result in a large number of images per patient, ranging from 250 to 300 depending on the patient’s size. Most exams are normal
T Corresponding author. E-mail address: [email protected] (H.C. Roberts). 0531-5131/ D 2005 CARS & Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2005.03.337
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or show non-specific findings; any nodule has to be reliably indicated in such a scan. It has been demonstrated that an unacceptable number of nodules may be missed by radiologist reading [5,6]. The issues of reader fatigue and the presentation of essentially redundant image appearances are addressed with the development of semi-automated computer-aided detection (CAD) software algorithms. Several different software tools are commercially available or in different stages of development, and have been tested based on varying imaging protocols [9–18]. The purpose of this study is to assess one of the commercially available tools, the ImageChecker-CT, from R2 Technology, within a state-of-the art screening study. 2. Methods 2.1. Patients and CT scans The University Health Network in Toronto is part of the International Early Lung Cancer Action Program (I-ELCAP), screening high-risk current or former smokers 55 years or older, with at least a 10-pack-year history of smoking. For this retrospective study, the first 250 low-dose CT studies of the screening cohort were analyzed. The study was approved by the local research ethics board. The CT studies were performed on a multislice helical CT scanner (General Electric Medical Systems, Milwaukee, WI). The scanning parameters (following the I-ELCAP protocol) were as follows: collimation 1.25 mm, 60 mA, 140 kV. 2.2. CT analysis All CT scans were read independently by two experienced chest radiologists (double read); their consensus served as the reference standard. Subsequently we applied the ImageChecker CT Server algorithm (R2 technologies), designed to detect lung nodules that are solid, parenchymal nodules in the lung tissue z 4 mm, or pleural-based nodules z 4 mm, provided they project significantly into the lung. The algorithm generally defines solid, parenchymal nodules as focal densities that are approximately spherical in shape, have boundaries that are smooth, lobulated, or spiculated, and have density close to that of soft tissue. Based on each finding’s size, shape, density and other characteristics, the algorithm assigns the finding a likelihood. All findings whose likelihood exceeds a threshold are presented to the user as markers. The algorithm does not look for non-nodular abnormalities (e.g., scars, atelectasis, bronchiectasis), nodules or other abnormalities outside of the lung parenchyma (e.g., in the mediastinum or in the chest wall), ground-glass opacities or other non-solid or part-solid nodules. All CAD entities were re-assessed by a trained radiologist, and characterized as follows: (a) true positive entity, TPE: solid density correctly identified by the software following the detection algorithm, but classified by the reader as not a nodule based on overall appearance; (b) true positive nodule, TPN: solid nodule found by CAD and confirmed by the radiological re-assessment whether or not identified on initial double-reading;
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(c) false positive, FP: any CAD-identified entity that was not a parenchymal density or nodule; (d) false negative, FN: any nodule found in the double-read, but not marked by CAD. 3. Results A total of 64389 sections in the 250 patients were analyzed (258 sections/patient), in which CAD marked 1136 entries. From the subsequent analysis, 196 entries were eliminated because they were obviously outside the lung (soft tissue, neck, jaw, shoulder, esophagus, abdomen, etc.) and could easily be rejected by the reader based on extraparenchymal location. Twenty-nine non-solid and 27 part-solid nodules, as well 124 calcified nodules were excluded from the analysis based on the algorithm definition by the company (see Methods). Even though the software is trained to mark nodules z 4 mm, the analysis was restricted to nodules z 5 mm, since in a previous publication from the IELCAP group only nodules 5 mm or larger were clinically significant and were recommended for short-term follow-up [23]. In this subgroup of solid nodules 5 mm or larger, 83 solid nodules had been found by the initial double-read, CAD marked an additional 21 nodules TPN (diagnostic yield 25%) (Fig. 1a). The sensitivity obtained by double read was 83/104 (80%). In this subgroup, CAD marked 856 entries. Of those, 117 (14%) were TP (TPN +TPE), 76 were TPN (9%), 739 (86%) were FP. The rate of FP findings was 0.01/section and approximately 3/patient. CAD missed 28 nodules (FN) found by the previous double-read (27%) of the 104 nodules present. The sensitivity obtained by CAD for nodules (TPN) was 76/104 (73%). The 21 TPN CAD findings missed by the double read had an average diameter of 7 mm, were mostly on the right (n = 16), 10 were in the upper lobe (right 7, left 3), 10 in the lower lobe (right 8, left 2), 1 in the middle lobe. Those 41 entities marked as TPE were predominantly pleura-based densities which based on the overall appearance had to be interpreted as pleural plaques, (sub-) pleural thickening or fibrosis, or as pleura-based atelectases. I.e., even though they were correctly identified by the CAD algorithm as a focal nodular density, their overall appearance did not warrant a follow-up. However, since they had sufficient amount of nodule-like
Fig. 1. (a) True positive CAD marking—nodule in right middle lobe. (b) True positive CAD entity—pleural thickening with nodular characteristics.
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characteristics, it did not seem to be justified to label these entities as FP algorithm detections (Fig. 1b). The majority of FP detections were atelectases (35%), fissures (14%), normal vessels (13%), pleura-based abnormalities (11%); less frequently were mediastinal vessels or structures (9%), apical fibrosis (6%), osteophytes (2%), pericardial fat (2%), or mucoid impaction (1%). Nineteen of the 28 FN entries were in the right lung, 9 in the left. Seventeen were in the upper lobes or lingula, 4 in the middle lobe and 7 in the lower lobes. Most of those were pleura-based (n = 13); their average size was 9 mm. Within these 250 patients, 5 had a cancer. CAD detected one of the cancers, 4 were not included in this analysis because they were of non-solid consistency (brochioloalveolar carcinoma), 1 was pleura-based and ill-defined and counted as FN. 4. Discussion Most of the previous publications have assessed the utility of CAD based on a different scanning technology, predominantly using thicker slices [19,20], or assessing smaller groups [19–22], or biasing the reader with some information on nodules present [19,20]. Even though most studies agree that the detection of nodules can be improved using CAD in conjunction with radiologist read, the number of generated false positives in the studies remains to be addressed. This statement seems to be even more valid when CAD is used in a state-of-the art screening protocol. With the thin slices obtained with low dose in the presented study, the rate of false positives increased to 86%, which may in part be due to the resulting image noise. False positive findings can be regarded as an inconvenience to the radiologist; each false positive CAD mark does focus the radiologist’s attention to a potential nodule-containing area that needs to be checked individually. By simple scrolling through the images a radiologist has to reveal its true nature, whether it is normal anatomy (vessels, mediastinal structures, fat or fissures) or unrelated, non-specific findings such as atelectases or scarring. This does not represent a diagnostic challenge to the radiologist, but it does cause a distraction and is time-consuming. We regard the rate of 86% false positives as a limitation for the overall usefulness of this CAD system. However, we can point toward the most difficult area that might need further software development and improvement: the subpleural region both along the costal pleura, the mediastinal surface, and the fissures. While the rate of false positives is an inconvenience, the rate of false negatives has to be regarded as a problem. Using CAD as a simultaneous, adjunct tool, the radiologist does trust the algorithm to point out every potentially interesting focal opacity, does reduce his/ her alertness and thus decreases his/her sensitivity. This procedure does require a low rate of false negative findings provided by CAD. The false negative rate of 27% in this study is too high to recommend CAD for an adjunct tool, but rather, CAD should be used as a subsequent tool after an initial radiologist’s read. This procedure is indeed recommended in the manufacturer’s manual. The downside is the added time that needs to be allocated for the repeat read, resulting rather in a decrease in overall patient throughput. Again, the most difficult area for this CAD software was the subpleural region, where most false negative findings were located.
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Nevertheless, the subsequent use of CAD in this study population did improve the detection rate of nodules found: CAD found 21 nodules missed by the initial radiologists’ read, implying that CAD is a complement to the radiologists and does find different nodules. Thus, CAD as a third reader seems to be warranted. Whether the added time for a third reading is appropriate remains to be tested in a larger screening cohort. Finally, we should consider the possibility of CAD as a second reader, saving the time of the second radiologist. In summary, the CAD tool tested in this lung cancer screening population did indeed improve the detection of pulmonary nodules compared to a double radiologists’ read. Most false positive results can be readily discarded based on anatomical location. Within the analyzed subgroup defined by the literature (=5mm) and the manufacturer (solid, noncalcified), sensitivity of standalone CAD is comparable to the sensitivity of double radiology read. However, the simultaneous use of CAD in lung cancer screening studies is still limited by the rate of false negatives. In the presented study, CAD was a good complement to a radiologist because of its tendency to find different nodules. References [1] E.F. Patz, Screening for lung cancer, N. Engl. J. Med. 343 (22) (2000) 1627 – 1633. [2] J.E. Heffner, G. Silvestri, CT screening for lung cancer: is smaller better? Am. J. Respir. Crit. Care Med. 165 (2002) 433 – 434. [3] J.R. Jett, E.F. Patz, Pro/con editorial: spiral computed tomography screening for lung cancer, Am. J. Respir. Crit. Care Med. 163 (2001) 812 – 815. [4] C.I. Henschke, et al., Early lung cancer action project: overall design and findings from baseline screening, Lancet 354 (1999) 99 – 105. [5] S.J. Swensen, et al., Screening for lung cancer with low-dose spiral CT, Am. J. Respir. Crit. Care Med. 165 (2002) 508 – 513. [6] C. McCulloch, et al., Reader variability and computer aided detection of suspicious lesions in low-dose CT lung screening exams, Radiology 226 (2003) 37A. [7] S. Sone, Mass screening for lung cancer with mobile spiral computed tomography scanner, Lancet 351 (1998) 1242 – 1245. [8] G. Bepler, et al., A systematic review and lessons learned from early lung cancer detection trials using lowdose computed tomography of the chest, Cancer Control 10 (2003) 306 – 314. [9] M.L. Giger, K.T. Bae, H. MacMahon, Computerized detection of pulmonary nodules in computed tomography images, Invest. Radiol. 29 (1994) 459 – 465. [10] P. Croisille, et al., Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT, Radiology 197 (1995) 397 – 401. [11] K. Kanazawa, et al., Computer-aided diagnosis for pulmonary nodules based on helical CT images, Comput. Med. Imaging Graph. 22 (1998) 157 – 167. [12] S.G. Armato, et al., Computerized detection of pulmonary nodules on CT scans, Radiographics 19 (1999) 1303 – 1311. [13] A.P. Reeves, W.J. Kostis, Computed-aided diagnosis of small pulmonary nodules, Semin. Ultrasound CT MR 21 (2000) 116 – 128. [14] A.P. Reeves, W.J. Kostis, Computed-aided diagnosis for lung cancer, Radiol. Clin. North Am. 38 (2000) 497 – 509. [15] Y. Lee, et al., Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique, IEEE Trans. Med. Imag. 20 (2001) 595 – 604. [16] J.P. Ko, M. Betke, C.T. Chest, Automated nodule detection and assessment of change over time— preliminary experience, Radiology 218 (2001) 267 – 273. [17] D. Wormanns, et al., Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system, Eur. Radiol. 12 (2002) 1052 – 1057.
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[18] S. Toshioka, et al., Computer aided diagnosis system for lung cancer based on helical CT images, Proc. SPIE 3034 (1997) 975 – 984. [19] K. Awai, et al., Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists’ detection performance, Radiology 230 (2004) 347 – 352. [20] S.G. Armato III, et al., Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program, Radiology 225 (2002) 685 – 692. [21] C.C. MCCulloch, et al., Model-based detection of lung nodules in computed tomography exams, Acad. Radiol. 11 (2004) 258 – 266. [22] D. Wormanns, et al., Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system, Eur. Radiol. 12 (2002) 1052 – 1057. [23] C.I. Henschke, et al., CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans, Radiology 231 (2004) 164 – 168.
www.ics-elsevier.com
The utility of computer-aided detection (CAD) for lung cancer screening using low-dose CT Heidi C. RobertsT, Demetris Patsios, Michael Kucharczyk, Narinder Paul, Timothy P. Roberts Department of Medical Imaging, University Health Network, Toronto, ON, Canada
Abstract. Multi-detector CT scanners facilitate the detection of very small nodules within the lung parenchyma, with a sensitivity that is significantly superior to that of chest radiography. It has been demonstrated that an unacceptable number of nodules may be missed by radiologist reading. The issues of reader fatigue and the presentation of essentially redundant image appearances are addressed with the development of semi-automated computer-aided detection (CAD) software algorithms. The purpose of this study is to assess one of the commercially available tools, the ImageChecker-CT, from R2 Technology, within a state-of-the art screening study. D 2005 CARS & Elsevier B.V. All rights reserved. Keywords: Lung cancer screening; CAD; Low-dose CT
1. Introduction Lung cancer screening using low-dose computed tomography has gained rising recognition in the last decade and–even though its utility remains heavily discussed [1–3]– is increasingly performed to detect low grade lung cancers in high-risk smokers [4–7]. Modern, state-of-the art technology utilizes multi-detector CT scanners, yielding thin (1– 1.5 mm) slices covering the entire chest. This protocol facilitates the detection of very small nodules within the lung parenchyma, with a sensitivity that is significantly superior to that of chest radiography [1,8]. However, it does result in a large number of images per patient, ranging from 250 to 300 depending on the patient’s size. Most exams are normal
T Corresponding author. E-mail address: [email protected] (H.C. Roberts). 0531-5131/ D 2005 CARS & Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2005.03.337
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or show non-specific findings; any nodule has to be reliably indicated in such a scan. It has been demonstrated that an unacceptable number of nodules may be missed by radiologist reading [5,6]. The issues of reader fatigue and the presentation of essentially redundant image appearances are addressed with the development of semi-automated computer-aided detection (CAD) software algorithms. Several different software tools are commercially available or in different stages of development, and have been tested based on varying imaging protocols [9–18]. The purpose of this study is to assess one of the commercially available tools, the ImageChecker-CT, from R2 Technology, within a state-of-the art screening study. 2. Methods 2.1. Patients and CT scans The University Health Network in Toronto is part of the International Early Lung Cancer Action Program (I-ELCAP), screening high-risk current or former smokers 55 years or older, with at least a 10-pack-year history of smoking. For this retrospective study, the first 250 low-dose CT studies of the screening cohort were analyzed. The study was approved by the local research ethics board. The CT studies were performed on a multislice helical CT scanner (General Electric Medical Systems, Milwaukee, WI). The scanning parameters (following the I-ELCAP protocol) were as follows: collimation 1.25 mm, 60 mA, 140 kV. 2.2. CT analysis All CT scans were read independently by two experienced chest radiologists (double read); their consensus served as the reference standard. Subsequently we applied the ImageChecker CT Server algorithm (R2 technologies), designed to detect lung nodules that are solid, parenchymal nodules in the lung tissue z 4 mm, or pleural-based nodules z 4 mm, provided they project significantly into the lung. The algorithm generally defines solid, parenchymal nodules as focal densities that are approximately spherical in shape, have boundaries that are smooth, lobulated, or spiculated, and have density close to that of soft tissue. Based on each finding’s size, shape, density and other characteristics, the algorithm assigns the finding a likelihood. All findings whose likelihood exceeds a threshold are presented to the user as markers. The algorithm does not look for non-nodular abnormalities (e.g., scars, atelectasis, bronchiectasis), nodules or other abnormalities outside of the lung parenchyma (e.g., in the mediastinum or in the chest wall), ground-glass opacities or other non-solid or part-solid nodules. All CAD entities were re-assessed by a trained radiologist, and characterized as follows: (a) true positive entity, TPE: solid density correctly identified by the software following the detection algorithm, but classified by the reader as not a nodule based on overall appearance; (b) true positive nodule, TPN: solid nodule found by CAD and confirmed by the radiological re-assessment whether or not identified on initial double-reading;
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(c) false positive, FP: any CAD-identified entity that was not a parenchymal density or nodule; (d) false negative, FN: any nodule found in the double-read, but not marked by CAD. 3. Results A total of 64389 sections in the 250 patients were analyzed (258 sections/patient), in which CAD marked 1136 entries. From the subsequent analysis, 196 entries were eliminated because they were obviously outside the lung (soft tissue, neck, jaw, shoulder, esophagus, abdomen, etc.) and could easily be rejected by the reader based on extraparenchymal location. Twenty-nine non-solid and 27 part-solid nodules, as well 124 calcified nodules were excluded from the analysis based on the algorithm definition by the company (see Methods). Even though the software is trained to mark nodules z 4 mm, the analysis was restricted to nodules z 5 mm, since in a previous publication from the IELCAP group only nodules 5 mm or larger were clinically significant and were recommended for short-term follow-up [23]. In this subgroup of solid nodules 5 mm or larger, 83 solid nodules had been found by the initial double-read, CAD marked an additional 21 nodules TPN (diagnostic yield 25%) (Fig. 1a). The sensitivity obtained by double read was 83/104 (80%). In this subgroup, CAD marked 856 entries. Of those, 117 (14%) were TP (TPN +TPE), 76 were TPN (9%), 739 (86%) were FP. The rate of FP findings was 0.01/section and approximately 3/patient. CAD missed 28 nodules (FN) found by the previous double-read (27%) of the 104 nodules present. The sensitivity obtained by CAD for nodules (TPN) was 76/104 (73%). The 21 TPN CAD findings missed by the double read had an average diameter of 7 mm, were mostly on the right (n = 16), 10 were in the upper lobe (right 7, left 3), 10 in the lower lobe (right 8, left 2), 1 in the middle lobe. Those 41 entities marked as TPE were predominantly pleura-based densities which based on the overall appearance had to be interpreted as pleural plaques, (sub-) pleural thickening or fibrosis, or as pleura-based atelectases. I.e., even though they were correctly identified by the CAD algorithm as a focal nodular density, their overall appearance did not warrant a follow-up. However, since they had sufficient amount of nodule-like
Fig. 1. (a) True positive CAD marking—nodule in right middle lobe. (b) True positive CAD entity—pleural thickening with nodular characteristics.
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characteristics, it did not seem to be justified to label these entities as FP algorithm detections (Fig. 1b). The majority of FP detections were atelectases (35%), fissures (14%), normal vessels (13%), pleura-based abnormalities (11%); less frequently were mediastinal vessels or structures (9%), apical fibrosis (6%), osteophytes (2%), pericardial fat (2%), or mucoid impaction (1%). Nineteen of the 28 FN entries were in the right lung, 9 in the left. Seventeen were in the upper lobes or lingula, 4 in the middle lobe and 7 in the lower lobes. Most of those were pleura-based (n = 13); their average size was 9 mm. Within these 250 patients, 5 had a cancer. CAD detected one of the cancers, 4 were not included in this analysis because they were of non-solid consistency (brochioloalveolar carcinoma), 1 was pleura-based and ill-defined and counted as FN. 4. Discussion Most of the previous publications have assessed the utility of CAD based on a different scanning technology, predominantly using thicker slices [19,20], or assessing smaller groups [19–22], or biasing the reader with some information on nodules present [19,20]. Even though most studies agree that the detection of nodules can be improved using CAD in conjunction with radiologist read, the number of generated false positives in the studies remains to be addressed. This statement seems to be even more valid when CAD is used in a state-of-the art screening protocol. With the thin slices obtained with low dose in the presented study, the rate of false positives increased to 86%, which may in part be due to the resulting image noise. False positive findings can be regarded as an inconvenience to the radiologist; each false positive CAD mark does focus the radiologist’s attention to a potential nodule-containing area that needs to be checked individually. By simple scrolling through the images a radiologist has to reveal its true nature, whether it is normal anatomy (vessels, mediastinal structures, fat or fissures) or unrelated, non-specific findings such as atelectases or scarring. This does not represent a diagnostic challenge to the radiologist, but it does cause a distraction and is time-consuming. We regard the rate of 86% false positives as a limitation for the overall usefulness of this CAD system. However, we can point toward the most difficult area that might need further software development and improvement: the subpleural region both along the costal pleura, the mediastinal surface, and the fissures. While the rate of false positives is an inconvenience, the rate of false negatives has to be regarded as a problem. Using CAD as a simultaneous, adjunct tool, the radiologist does trust the algorithm to point out every potentially interesting focal opacity, does reduce his/ her alertness and thus decreases his/her sensitivity. This procedure does require a low rate of false negative findings provided by CAD. The false negative rate of 27% in this study is too high to recommend CAD for an adjunct tool, but rather, CAD should be used as a subsequent tool after an initial radiologist’s read. This procedure is indeed recommended in the manufacturer’s manual. The downside is the added time that needs to be allocated for the repeat read, resulting rather in a decrease in overall patient throughput. Again, the most difficult area for this CAD software was the subpleural region, where most false negative findings were located.
H.C. Roberts et al. / International Congress Series 1281 (2005) 1137–1142
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Nevertheless, the subsequent use of CAD in this study population did improve the detection rate of nodules found: CAD found 21 nodules missed by the initial radiologists’ read, implying that CAD is a complement to the radiologists and does find different nodules. Thus, CAD as a third reader seems to be warranted. Whether the added time for a third reading is appropriate remains to be tested in a larger screening cohort. Finally, we should consider the possibility of CAD as a second reader, saving the time of the second radiologist. In summary, the CAD tool tested in this lung cancer screening population did indeed improve the detection of pulmonary nodules compared to a double radiologists’ read. Most false positive results can be readily discarded based on anatomical location. Within the analyzed subgroup defined by the literature (=5mm) and the manufacturer (solid, noncalcified), sensitivity of standalone CAD is comparable to the sensitivity of double radiology read. However, the simultaneous use of CAD in lung cancer screening studies is still limited by the rate of false negatives. In the presented study, CAD was a good complement to a radiologist because of its tendency to find different nodules. References [1] E.F. Patz, Screening for lung cancer, N. Engl. J. Med. 343 (22) (2000) 1627 – 1633. [2] J.E. Heffner, G. Silvestri, CT screening for lung cancer: is smaller better? Am. J. Respir. Crit. Care Med. 165 (2002) 433 – 434. [3] J.R. Jett, E.F. Patz, Pro/con editorial: spiral computed tomography screening for lung cancer, Am. J. Respir. Crit. Care Med. 163 (2001) 812 – 815. [4] C.I. Henschke, et al., Early lung cancer action project: overall design and findings from baseline screening, Lancet 354 (1999) 99 – 105. [5] S.J. Swensen, et al., Screening for lung cancer with low-dose spiral CT, Am. J. Respir. Crit. Care Med. 165 (2002) 508 – 513. [6] C. McCulloch, et al., Reader variability and computer aided detection of suspicious lesions in low-dose CT lung screening exams, Radiology 226 (2003) 37A. [7] S. Sone, Mass screening for lung cancer with mobile spiral computed tomography scanner, Lancet 351 (1998) 1242 – 1245. [8] G. Bepler, et al., A systematic review and lessons learned from early lung cancer detection trials using lowdose computed tomography of the chest, Cancer Control 10 (2003) 306 – 314. [9] M.L. Giger, K.T. Bae, H. MacMahon, Computerized detection of pulmonary nodules in computed tomography images, Invest. Radiol. 29 (1994) 459 – 465. [10] P. Croisille, et al., Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT, Radiology 197 (1995) 397 – 401. [11] K. Kanazawa, et al., Computer-aided diagnosis for pulmonary nodules based on helical CT images, Comput. Med. Imaging Graph. 22 (1998) 157 – 167. [12] S.G. Armato, et al., Computerized detection of pulmonary nodules on CT scans, Radiographics 19 (1999) 1303 – 1311. [13] A.P. Reeves, W.J. Kostis, Computed-aided diagnosis of small pulmonary nodules, Semin. Ultrasound CT MR 21 (2000) 116 – 128. [14] A.P. Reeves, W.J. Kostis, Computed-aided diagnosis for lung cancer, Radiol. Clin. North Am. 38 (2000) 497 – 509. [15] Y. Lee, et al., Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique, IEEE Trans. Med. Imag. 20 (2001) 595 – 604. [16] J.P. Ko, M. Betke, C.T. Chest, Automated nodule detection and assessment of change over time— preliminary experience, Radiology 218 (2001) 267 – 273. [17] D. Wormanns, et al., Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system, Eur. Radiol. 12 (2002) 1052 – 1057.
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[18] S. Toshioka, et al., Computer aided diagnosis system for lung cancer based on helical CT images, Proc. SPIE 3034 (1997) 975 – 984. [19] K. Awai, et al., Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists’ detection performance, Radiology 230 (2004) 347 – 352. [20] S.G. Armato III, et al., Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program, Radiology 225 (2002) 685 – 692. [21] C.C. MCCulloch, et al., Model-based detection of lung nodules in computed tomography exams, Acad. Radiol. 11 (2004) 258 – 266. [22] D. Wormanns, et al., Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system, Eur. Radiol. 12 (2002) 1052 – 1057. [23] C.I. Henschke, et al., CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans, Radiology 231 (2004) 164 – 168.