HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Nakamura, K. Articles by Doi, K. Search for Related Content PubMed PubMed Citation Articles by Nakamura, K. Articles by Doi, K.

Computerized Analysis of the Likelihood of Malignancy in Solitary Pulmonary Nodules with Use of Artificial Neural Networks¹

¹ From the Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image Research, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637. Received November 25, 1998; revision requested December 29; final revision received August 30, 1999; accepted September 2. Supported in part by United States Public Health Service grants CA24806 and CA62625. Address reprint requests to K.D. (e-mail: k-doi@uchicago.edu).

PURPOSE: To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) to assist radiologists in the distinction of benign and malignant pulmonary nodules.

MATERIALS AND METHODS: Fifty-six chest radiographs of 34 primary lung cancers and 22 benign nodules were digitized with a 0.175-mm pixel size and a 10-bit gray scale. Eight subjective image features were evaluated and recorded by radiologists in each case. A computerized method was developed to extract objective features that could be correlated with the subjective features. An ANN was used to distinguish benign from malignant nodules on the basis of subjective or objective features. The performance of the ANN was compared with that of the radiologists by means of receiver operating characteristic (ROC) analysis.

RESULTS: Performance of the ANN was considerably greater with objective features (area under the ROC curve, A_z = 0.854) than with subjective features (A_z = 0.761). Performance of the ANN was also greater than that of the radiologists (A_z = 0.752).

CONCLUSION: The computerized scheme has the potential to improve the diagnostic accuracy of radiologists in the distinction of benign and malignant solitary pulmonary nodules.

Index terms: Computers, neural network • Computers, diagnostic aid • Diagnostic radiology, observer performance • Lung neoplasms, diagnosis, 60.11, 60.31, 60.321 • Lung, nodule, 60.281 • Receiver operating characteristic (ROC) curve

This article has been cited by other articles:

				K. Yamashita, T. Yoshiura, H. Arimura, F. Mihara, T. Noguchi, A. Hiwatashi, O. Togao, Y. Yamashita, T. Shono, S. Kumazawa, et al. Performance Evaluation of Radiologists with Artificial Neural Network for Differential Diagnosis of Intra-Axial Cerebral Tumors on MR Images AJNR Am. J. Neuroradiol., June 1, 2008; 29(6): 1153 - 1158. [Abstract] [Full Text] [PDF]

				E M Schultz, G D Sanders, P R Trotter, E F Patz Jr, G A Silvestri, D K Owens, and M K Gould Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules Thorax, April 1, 2008; 63(4): 335 - 341. [Abstract] [Full Text] [PDF]

				M. K. Gould, J. Fletcher, M. D. Iannettoni, W. R. Lynch, D. E. Midthun, D. P. Naidich, and D. E. Ost Evaluation of Patients With Pulmonary Nodules: When Is It Lung Cancer?: ACCP Evidence-Based Clinical Practice Guidelines (2nd Edition) Chest, September 1, 2007; 132(3_suppl): 108S - 130S. [Abstract] [Full Text] [PDF]

				Y. J. Jeong, C. A. Yi, and K. S. Lee Solitary Pulmonary Nodules: Detection, Characterization, and Guidance for Further Diagnostic Workup and Treatment Am. J. Roentgenol., January 1, 2007; 188(1): 57 - 68. [Abstract] [Full Text] [PDF]

				Y. Nie, Q. Li, F. Li, Y. Pu, D. Appelbaum, and K. Doi Integrating PET and CT Information to Improve Diagnostic Accuracy for Lung Nodules: A Semiautomatic Computer-Aided Method J. Nucl. Med., July 1, 2006; 47(7): 1075 - 1080. [Abstract] [Full Text] [PDF]

				K Doi Current status and future potential of computer-aided diagnosis in medical imaging Br. J. Radiol., January 1, 2005; 78(suppl_1): S3 - s19. [Abstract] [Full Text] [PDF]

				F. Li, M. Aoyama, J. Shiraishi, H. Abe, Q. Li, K. Suzuki, R. Engelmann, S. Sone, H. MacMahon, and K. Doi Radiologists' Performance for Differentiating Benign from Malignant Lung Nodules on High-Resolution CT Using Computer-Estimated Likelihood of Malignancy Am. J. Roentgenol., November 1, 2004; 183(5): 1209 - 1215. [Abstract] [Full Text] [PDF]

				A. Fukushima, K. Ashizawa, T. Yamaguchi, N. Matsuyama, H. Hayashi, I. Kida, Y. Imafuku, A. Egawa, S. Kimura, K. Nagaoki, et al. Application of an Artificial Neural Network to High-Resolution CT: Usefulness in Differential Diagnosis of Diffuse Lung Disease Am. J. Roentgenol., August 1, 2004; 183(2): 297 - 305. [Abstract] [Full Text] [PDF]

				J. Shiraishi, H. Abe, R. Engelmann, M. Aoyama, H. MacMahon, and K. Doi Computer-aided Diagnosis to Distinguish Benign from Malignant Solitary Pulmonary Nodules on Radiographs: ROC Analysis of Radiologists' Performance--Initial Experience Radiology, May 1, 2003; 227(2): 469 - 474. [Abstract] [Full Text] [PDF]

				B. B. Tan, K. R. Flaherty, E. A. Kazerooni, and M. D. Iannettoni The Solitary Pulmonary Nodule Chest, January 1, 2003; 123(1_suppl): 89S - 96S. [Abstract] [Full Text] [PDF]

				H. Abe, H. MacMahon, R. Engelmann, Q. Li, J. Shiraishi, S. Katsuragawa, M. Aoyama, T. Ishida, K. Ashizawa, C. E. Metz, et al. Computer-aided Diagnosis in Chest Radiography: Results of Large-Scale Observer Tests at the 1996-2001 RSNA Scientific Assemblies RadioGraphics, January 1, 2003; 23(1): 255 - 265. [Abstract] [Full Text] [PDF]

				A. M. Evancho, H. Yoshida, and A. Dachman Computer-aided Diagnosis: Blessing or Curse? * Drs Yoshida and Dachman respond: Radiology, November 1, 2002; 225(2): 606 - 607. [Full Text] [PDF]

				J. Eng Predicting the Presence of Acute Pulmonary Embolism: A Comparative Analysis of the Artificial Neural Network, Logistic Regression, and Threshold Models Am. J. Roentgenol., October 1, 2002; 179(4): 869 - 874. [Abstract] [Full Text] [PDF]

				Y. Matsuki, K. Nakamura, H. Watanabe, T. Aoki, H. Nakata, S. Katsuragawa, and K. Doi Usefulness of an Artificial Neural Network for Differentiating Benign from Malignant Pulmonary Nodules on High-Resolution CT: Evaluation with Receiver Operating Characteristic Analysis Am. J. Roentgenol., March 1, 2002; 178(3): 657 - 663. [Abstract] [Full Text] [PDF]