Construction of a Bayesian network for mammographic diagnosis of breast cancer

Computers in Biology and Medicine

Volumn 27, Issue 1, 1997, Pages 19-29

  Kahn Jr , Charles E ^a   Roberts, Linda M ^b   Shaffer, Katherine A ^a   Haddawy, Peter ^a  

^a MEDICAL COLLEGE OF WISCONSIN   (United States)

^b University of Wisconsin Milwaukee   (United States)

Author keywords

artificial intelligence; Bayesian networks; breast cancer; computer aided diagnosis; expert systems; mammography

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; BREAST CANCER; COMPUTER ASSISTED DIAGNOSIS; EXPERT SYSTEM; MAMMOGRAPHY; PRIORITY JOURNAL;

BAYES THEOREM; BREAST NEOPLASMS; DATA COLLECTION; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MAMMOGRAPHY; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); SOFTWARE;

EID: 0030982828     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0010-4825(96)00039-X     Document Type: Article

References (58)

1. 1. P.A. Wingo, T. Tong and S. Bolden, Cancer statistics, 1995, CA, 45, 8 (1995).
2. 2. S.A. Feig, Decreased cancer mortality through mammographic screening: results of clinical trials, Radiol., 167, 659 (1988).
3. 3. F.M. Hall, J.M. Storella, D.Z. Silverstone and G. Wyshak, Nonpalpable breast lesions: recommendations for biopsy based on suspicion of carcinoma at mammography, Radiol., 167, 353 (1988).
4. 4. D.J. Getty, R.M. Pickett, C.J. D'Orsi and J.A. Swets, Enhanced interpretation of diagnostic images, Invest. Radiol., 23, 240 (1988).
5. 5. Y. Wu et al., Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer, Radiol., 187, 81 (1993).
6. 6. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA (1988).
7. 7. S.L. Lauritzen and D.J. Spiegelhalter, Local computations with probabilities on graphical structures and their applications to expert systems, J. R. Statist. Soc., 50, 157 (1988).
8. 8. S.K. Andersen, K.G. Olesen, F.V. Jensen and F. Jensen, HUGIN - a shell for building Bayesian belief universes for expert systems, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (AAAI Press, Menlo Park, CA, 1989), pp. 1080 (1989).
9. 9. S. Srinivas and J. Breese, IDEAL: a software package for analysis of influence diagrams, Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (Morgan Kaufmann, San Mateo, CA, 1990), pp. 212 (1990).
10. 10. S. Andreassen, F.V. Jensen and K.G. Olesen, Medical expert systems based on causal probabilistic networks, Int. J. Biomed. Comput., 28, 1 (1991).
11. 11. S. Andreassen, Planning of therapy and tests in causal probabilistic networks, Art. Intell. Med., 4, 227 (1992).
12. 12. P. Haddawy, C.E. Kahn Jr and M. Butarbutar, A Bayesian network model for radiological diagnosis and procedure selection: work-up of suspected gallbladder disease, Med. Phys., 21, 1185 (1994).
13. 13. S. Andreassen, M. Woldbye, B. Falcka and S.K. Andersen, MUNIN - a causal probabilistic network for interpretation of electromyographic findings, Proceedings of the International Joint Conference on Artificial Intelligence (AAAI Press, Menlo Park, CA, 1987), pp. 366 (1987).
14. 14. P.W. Hamilton, N. Anderson, P.H. Bartels and D. Thompson, Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast, J. Clin. Path., 47, 329 (1994).
15. 15. D.E. Heckerman, E.J. Horvitz and B.N. Nathwani, Toward normative expert systems: Part I. The Pathfinder project, Meth. Inform. Med., 31, 90 (1992).
16. 16. D.E. Heckerman and B.N. Nathwani, Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inference, Meth. Inform. Med., 31, 106 (1992).
17. 17. D.E. Heckerman and B.N. Nathwani, An evaluation of the diagnostic accuracy of Pathfinder, Comput. Biomed. Res., 25, 56 (1992).
18. 18. R. Tombropoulos, S. Shiffman and C. Davidson, A decision aid for diagnosis of liver lesions on MRI, C. Safran (Ed.), Proceedings of the 17th Annual Symposium of Computer Applications in Medical Care (McGraw-Hill, New York, 1993), pp. 439 (1993).
19. 19. Y.C. Li, P.J. Haug and H.R. Warner, Automated transformation of probabilistic knowledge for a medical diagnostic system, J.P. Ozbolt (Ed.), Proceedings of the 18th Annual Symposium on Computer Applications in Medical Care (Hanley and Belfus, Philadelphia, 1994), pp. 765 (1994).
20. 20. M.A. Shwe et al., Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms, Meth. Inform. Med., 30, 241 (1991).
21. 21. B. Middleton et al., Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance, Meth. Inform. Med., 30, 256 (1991).
22. 22. S.A. Feig, Breast masses: mammographic and sonographic evaluation, Radiol. Clin. N. Amer., 30, 67 (1992).
23. 23. G.A. Colditz et al., Family history, age, and risk of breast cancer. Prospective data from the Nurses' Health Study, J. Am. Med. Ass., 270, 338 (1993).
24. 24. R.E. Symmonds Jr and J.W. Roberts, Management of nonpalpable breast abnormalities, Ann. Surg., 205, 520 (1987).
25. 25. E.A. Sickles, Breast masses: mammographic evaluation, Radiol., 173, 297 (1989).
26. 26. C.A. Swann et al., The halo sign and malignant breast lesions, Am. J. Roentgen., 149, 1145 (1987).
27. 27. L.W. Bassett, Mammographic analysis of calcifications, Radiol. Clin. N. Amer., 30, 93 (1992).
28. 28. E.A. Sickles, Breast calcifications: mammographic evaluation, Radiol., 160, 289 (1986).
29. 29. E.A. Sickles, Mammographic features of 300 consecutive nonpalpable breast cancers, Am. J. Roentgen., 146, 661 (1986).
30. 30. C.J. D'Orsi and D.B. Kopans, Mammographic feature analysis, Seminars in Roentgenology, 28, 204 (1993).
31. 31. Breast Imaging Reporting and Data System (BI-RADS). American College of Radiology, Reston, VA (1993).
32. 32. J.L. Kelsey and M.D. Gammon, The Epidemiology of Breast Cancer. American Cancer Society, New York (1991).
33. 33. M.H. Gail et al., Projecting individualized probabilities of developing breast cancer for white females who are being examined annually, J. Natl Canc. Inst., 81, 1879 (1989).
34. 34. E.A. Sickles, Nonpalpable, circumscribed, noncalcified solid breast masses: likelihood of malignancy based on lesion size and age of patient, Radiol., 192, 439 (1994).
35. 35. R.L. Egan, M.B. McSweeney and C.W. Sewell, Intramammary calcifications without an associated mass in benign and malignant diseases, Radiol., 137, 1 (1980).
36. 36. V.P. Jackson, K.A. Dines, L.W. Bassett, R.H. Gold and H.E. Reynolds, Diagnostic importance of the radiographic density of noncalcified breast masses: analysis of 91 lesions, Amer. J. Roentgen., 157, 25 (1991).
37. 37. P. Haddawy, Generating Bayesian networks from probability logic knowledge bases, Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (Morgan Kaufmann, San Mateo, CA, 1994), pp. 262 (1994).
38. 38. P. Haddawy, L. Ngo, J. W. Helwig and R. Krieger, Clinical simulation using context-sensitive temporal probability models, R.M. Gardner (Ed.), Proceedings of the 19th Annual Symposium on Computer Applications in Medical Care (Hanley and Belfus, Philadelphia, 1995), pp. 203 (1995).
39. 39. L. Ngo, P. Haddawy, J. Helwig and R.A. Krieger, Efficient temporal probabilistic reasoning via context-sensitive model construction, Comput. Biol. Med. (in press).
40. 40. L. Ngo and P. Haddawy, Probabilistic logic programming and Bayesian networks, Algorithms, Concurrency and Knowledge (Proceedings ACSC95) (Springer-Verlag, 1995), pp. 286 (1995).
41. 41. L. Ngo and P. Haddawy, Answering queries from context-sensitive probabilistic knowledge bases, Theoret. Comput. Sci., 171, 147 (1997).
42. 42. L. Tabár and P.B. Dean, Teaching Atlas of Mammography. Thieme-Stratton, New York (1985).
43. 43. C.E. Metz, Some practical issues of experimental design and data analysis in radiological ROC studies, Invest. Radiol., 24, 234 (1989).
44. 44. Y. Wu, K. Doi, M.L. Giger and R.M. Nishikawa, Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks, Med. Phys., 19, 555 (1992).
45. 45. C.A. Beam, P.M. Layde and D.C. Sullivan, Variability in the interpretations of screening mammograms by US radiologists: findings from a national sample, Arch. Int. Med., 156, 209 (1995).
46. 46. C.E. Metz, ROC methodology in radiologic imaging, Invest. Radiol., 21, 720 (1986).
47. 47. C.J. D'Orsi et al., Reading and decision aids for improved accuracy and standardization of mammographic diagnosis, Radiol., 184, 619 (1992).
48. 48. C.E. Floyd Jr, J.Y. Lo, A.J. Yun, D.C. Sullivan and P.J. Kornguth, Prediction of breast cancer malignancy using an artificial neural network, Cancer, 74, 2944 (1994).
49. 49. J.A. Baker, P.J. Kornguth, J.Y. Lo, M.E. Williford and C.E. Floyd Jr, Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon, Radiol., 196, 817 (1995).
50. 50. J.Y. Lo, J.A. Baker, P.J. Kornguth and C.E. Floyd Jr, Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features, Acad. Radiol., 2, 841 (1995).
51. 51. J.A. Baker, P.J. Kornguth, J.Y. Lo and C.E. Floyd Jr, Artificial neural network: improving the quality of breast biopsy recommendations, Radiol., 198, 131 (1996).
52. 52. R.M. Nishikawa etal.,, Effect of case selection on the performance of computer-aided detection schemes, Med. Phys., 21, 265 (1994).
53. 53. J. Diederich, Explanation and artificial neural networks, Int. J. Man-Machine Stud., 37, 335 (1992).
54. 54. R.L. Teach and E.H. Shortliffe, An analysis of physician attitudes regarding computer-based clinical consultation systems, Comput. Biomed. Res., 14, 542 (1981).
55. 55. M. Henrion and M.J. Druzdzel, Qualitative propagation and scenario-based approaches to explanation of probabilistic reasoning. In P.P. Bonissone, M. Henrion, L.N. Kanal and J.F. Lemmer (Eds), Uncertainty in Artificial Intelligence 6 (Elsevier Science Publishers B.V., New York, 1991), pp. 17 (1991).
56. 56. H.J. Suermondt and G.F. Cooper, An evaluation of explanations of probabilistic inference, Comput. Biomed. Res., 26, 242 (1993).
57. 57. P. Haddawy, J. Jacobson and C.E. Kahn Jr, Generating explanations and tutorial problems from Bayesian networks, J.P. Ozbolt (Ed.), Proceedings of the 18th Annual Symposium on Computer Applications in Medical Care (Hanley and Belfus, Philadelphia, 1994), pp. 770 (1994).
58. 58. P. Haddawy, J. Jacobson and C.E. Kahn Jr, BANTER: a Bayesian network tutoring shell, Artif. Intell. Med. (in press).