Neural Network Ensemble-Based Computer-Aided Diagnosis for Differentiation of Lung Nodules on CT Images. Clinical Evaluation

Academic Radiology

Volumn 17, Issue 5, 2010, Pages 595-602

  Chen, Hui ^a   Xu, Yan ^b   Ma, Yujing ^a   Ma, Binrong ^a  

^a CAPITAL MEDICAL UNIVERSITY   (China)

^b CAPITAL MEDICAL UNIVERSITY   (China)

Author keywords

computed tomography (CT); Computer aided diagnosis (CAD); lung nodule; neural network ensemble (NNE); ROC curve

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; HUMAN; LUNG NODULE; MEDICAL DECISION MAKING; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; TUMOR VOLUME;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; DIAGNOSIS, DIFFERENTIAL; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LUNG NEOPLASMS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE;

EID: 77950460308     PISSN: 10766332     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.acra.2009.12.009     Document Type: Article

References (42)

1. Flehinger B.J., Kimmel M., and Melamed M.R. The effect of surgical treatment on survival from early lung cancer: implication for screening. Chest 101 (1992) 1013-1018
2. Sobue T., Suzuki R., Matsuda M., et al. Survival for clinical stage I lung cancer not surgically treated. Cancer 69 (1992) 685-692
3. Mittinen O.S. Screening for lung cancer. Radiol Clin N Am 38 (2000) 479-496
4. Matsuki Y., Nakamura K., Watanabe H., et al. 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 178 (2002) 657-663
5. Shiraishi J., Abe H., Engelmann R., et al. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance-initial experience. Radiology 227 (2003) 469-474
6. Sluimer I.C., van Waes P.F., Viergever M.A., et al. Computer-aided diagnosis in high resolution CT of the lungs. Med Phys 30 (2003) 3081-3090
7. Aoyama M., Li Q., Katsuragawa S., et al. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. Med Phys 30 (2003) 387-394
8. Li F., Aoyama M., Shiraishi J., et al. Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. Am J Roentgenol 183 (2004) 1209-1215
9. Li Q., Li F., Suzuki K., et al. Computer-aided diagnosis in thoracic CT. Semin Ultrasound CT 26 (2005) 357-363
10. Shah S.K., McNitt-Gray M.F., Rogers S.R., et al. Computer-aided diagnosis of the solitary pulmonary nodule. Acad Radiol 12 (2005) 570-575
11. Suzuki K., Li F., Sone S., et al. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE T Med Imaging 24 (2005) 1138-1150
12. Shiraishi J., Abe H., Li F., et al. Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs: ROC analysis of radiologists' performance. Acad Radiol 13 (2006) 995-1003
13. Awai K. Pulmonary Nodules: estimation of malignancy at thin-section helical CT-effect of computer-aided diagnosis on performance of radiologists. Radiology 239 (2006) 276-284
14. Chen H., Wang X.H., Ma D.Q., et al. Neural network-based computer-aided diagnosis in distinguishing malignant from benign solitary pulmonary nodules by computed tomography. Chinese Med J-Peking 120 (2007) 1211-1215
15. Silva E.C., Silva A.C., Paiva A.C., et al. Diagnosis of lung nodule using Moran's index and Geary's coefficient in computerized tomography images. Pattern Anal Applic 11 (2008) 89-99
16. Yeh C., Lin C.L., Wu M.T., et al. A neural network-based diagnostic method for solitary pulmonary nodules. Neurocomputing 72 (2008) 612-624
17. Way T.W. Computer-aided diagnosis of pulmonary nodules in thoracic computed tomography (2008), Doctoral Dissertation, University of Michigan 138-165
18. Way T.W., Sahiner B., Chan H.P., et al. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys 36 (2009) 3086-3098
19. Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 78 (2005) S3-S19
20. Armato S.G., McLennan G., McNitt-Gray M.F., et al. Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232 (2004) 739-748
21. National Biomedical Imaging Archive. Available at:. https://imaging.nci.nih.gov/ncia Accessed May 21, 2009
22. Canny J. A computational approach to edge detection. IEEE T Pattern Anal 8 (1986) 679-698
23. Giorgio G., and Fabio R. Design of effective neural network ensembles for image classification purposes. Image Vision Comput 19 (2001) 699-707
24. Lam L., and Suen C.Y. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE T Syst Man Cy A 27 (1997) 553-568
25. Partridge D. Network generalization differences quantified. Neural Networks 9 (1996) 263-271
26. Asuncion A., and Newman D.J. UCI Machine Learning Repository. Available at:. http://www.ics.uci.edu/∼mlearn/MLRepository.html Accessed July 21, 2008
27. Metz C.E., Herman B.A., and Shen J.H. Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. Stat Med 17 (1998) 1033-1053
28. Pan X., and Metz C.E. The "proper" binormal model: parametric ROC curve estimation with degenerate data. Acad Radiol 4 (1997) 380-389
29. McClish D.K. Analyzing a portion of the ROC curve. Med Decis Making 9 (1989) 190-195
30. Obuchowski N.A. Receiver operating characteristic curves and their use in radiology. Radiology 229 (2003) 3-8
31. Jiang Y., Metz C.E., and Nishikawa R.M. A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology 201 (1996) 745-750
32. Daly L., and Bourke G.J. Interpretation and uses of medical statistics. 5th ed. (2000), Wiley-Blackwell, Hoboken, NJ 410
33. Das R., Turkoglu I., and Sengur A. Diagnosis of valvular heart disease through neural networks ensembles. Comput Meth Prog Bio 39 (2009) 185-191
34. Osowski S., Markiewicz T., and Hoai L.T. Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement 41 (2008) 610-617
35. Daskalakis A., Kostopoulos S., Spyridonos P., et al. Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images. Comput Biol Med 38 (2008) 196-203
36. Zhou Z.H., Jiang Y., Yang Y.B., et al. Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 24 (2002) 25-36
37. Shah S.K., McNitt-Gray M.F., De Zoysa K.R., et al. Solitary pulmonary nodule diagnosis on CT: results of an observer study. Acad Radiol 12 (2005) 496-501
38. Erasmus J.J., McAdams H.P., and Connolly J.E. Solitary pulmonary nodules. Part II. Evaluation of the indeterminate nodule. Radio Graphics 20 (2000) 59-66
39. Swensen S.J., Viggiano R.W., Midthun D.E., et al. Lung nodule enhancement at CT: multicenter study. Radiology 214 (2000) 73-80
40. Yi C.A., Lee K.S., Kim E.A., et al. Solitary pulmonary nodules: dynamic enhanced multi-detector row CT study and comparison with vascular endothelial growth factor and microvessel density. Radiology 233 (2004) 191-199
41. Jeong Y.J., Lee K.S., Jeong S.Y., et al. Solitary pulmonary nodule: characterization with combined washin and washout features at dynamic multi-detector row CT. Radiology 237 (2005) 675-683
42. Lowe V.J., Fletcher J.W., Gobar L., et al. Prospective investigation of positron emission tomography in lung nodules. J Clin Oncol 16 (1998) 1075-1084