Korean J Radiol.  2005 Jun;6(2):89-93. 10.3348/kjr.2005.6.2.89.

Lung Nodule Detection on Chest CT: Evaluation of a Computer-Aided Detection (CAD) System

Affiliations
  • 1Department of Radiology, Weill Medical College of Cornell University, New York, NY. ggamsu@med.cornell.edu
  • 2Department of Radiology, Hallym University College of Medicine, Seoul, Korea.
  • 3Department of Radiology, Chinese Academy of Medical Science, Beijing, China.

Abstract


OBJECTIVE
To evaluate the capacity of a computer-aided detection (CAD) system to detect lung nodules in clinical chest CT. MATERIALS AND METHODS: A total of 210 consecutive clinical chest CT scans and their reports were reviewed by two chest radiologists and 70 were selected (33 without nodules and 37 with 1-6 nodules, 4-15.4 mm in diameter). The CAD system (ImageChecker (R) CT LN-1000) developed by R2 Technology, Inc. (Sunnyvale, CA) was used. Its algorithm was designed to detect nodules with a diameter of 4-20 mm. The two chest radiologists working with the CAD system detected a total of 78 nodules. These 78 nodules form the database for this study. Four independent observers interpreted the studies with and without the CAD system. RESULTS: The detection rates of the four independent observers without CAD were 81% (63/78), 85% (66/78), 83% (65/78), and 83% (65/78), respectively. With CAD their rates were 87% (68/78), 85% (66/78), 86% (67/78), and 85% (66/78), respectively. The differences between these two sets of detection rates did not reach statistical significance. In addition, CAD detected eight nodules that were not mentioned in the original clinical radiology reports. The CAD system produced 1.56 false-positive nodules per CT study. The four test observers had 0, 0.1, 0.17, and 0.26 false-positive results per study without CAD and 0.07, 0.2, 0.23, and 0.39 with CAD, respectively. CONCLUSION: The CAD system can assist radiologists in detecting pulmonary nodules in chest CT, but with a potential increase in their false positive rates. Technological improvements to the system could increase the sensitivity and specificity for the detection of pulmonary nodules and reduce these false-positive results.

Keyword

Lung nodule detection; Computed tomography (CT) ; Computer-aided detection

MeSH Terms

*Diagnosis, Computer-Assisted
False Positive Reactions
Humans
Lung Diseases/*radiography
Lung Neoplasms/radiography
Radiography, Thoracic/*methods
Sensitivity and Specificity
Tomography, X-Ray Computed/*methods

Reference

1. Wormanns D, Fiebich M, Saidi M, Diederich S, Heindel W. Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system. Eur Radiol. 2002. 12:1052–1057.
2. Armato SG 3rd, Li F, Giger ML, MacMahon H, Sone S, Doi K. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology. 2002. 225:685–692.
3. Kim DY, Kim JH, Noh SM, Park JW. Pulmonary nodule detection using chest CT images. Acta Radiol. 2003. 44:252–257.
4. Giger ML, Bae KT, MacMahon H. Computerized detection of pulmonary nodules in computed tomography images. Invest Radiol. 1994. 29:459–465.
5. Gurean MN, Sahiner B, Petrick N, Chan HP, Kazerooni EA, Cascade PN, et al. Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys. 2002. 29:2552–2558.
6. Awai K, Murao K, Ozawa A, Komi M, Hayakawa H, Hori S, et al. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. Radiology. 2004. 230:347–352.
7. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging. 2001. 20:595–604.
8. Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, et al. Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput Med Imaging Graph. 1998. 22:157–167.
9. Zhao B, Gamsu G, Ginsberg MS, Jiang L, Schwartz LH. Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J Appl Clin Med Phys. 2003. 4:248–260.
10. Henschke CI, Yankelevitz DF, Naidich DP, McCauley DI, McGuinness G, Libby DM, et al. CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans. Radiology. 2004. 231:164–168.
11. Diederich S, Semil M, Lentschig MG, Winter F, Scheld HH, Roos N, et al. Helical CT of pulmonary nodules in patients with extrathoracic malignancy: CT-surgical correlation. AJR Am J Roentgenol. 1999. 172:353–360.
12. Davis SD. CT evaluation for pulmonary metastases in patients with extrathoracic malignancy. Radiology. 1991. 180:1–12.
13. Kradin RL, Spirn PW, Mark EJ. Intrapulmonary lymph nodes. Clinical, radiologic, and pathologic features. Chest. 1985. 87:662–667.
Full Text Links
  • KJR
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr