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Journal of Digital Imaging logoLink to Journal of Digital Imaging
. 2001 Jun;14(Suppl 1):56–57. doi: 10.1007/BF03190296

A Bayesian network for diagnosis of primary bone tumors

Charles E Kahn Jr 1,, John J Laur 1, G F Carrera 1
PMCID: PMC3452681  PMID: 11442121

Abstract

The authors developed a Bayesian network to differentiate among five benign and five malignant neoplasms of the appendicular skeleton using the patient’s age and sex and 17 radiographic characteristics. In preliminary evaluation with physicians in training, the model identified the correct diagnosis in 19 cases (68%), and included the correct diagnosis among the two most probable diagnoses in 25 cases (89%). Bayesian networks can capture and apply knowledge of primary bone neoplasms. Further testing and refinement of the model are underway.

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References

  • 1.American Cancer Society: Bone Cancer Resource Center, http://www3.cancer.org/cancerinfo/load_cont.asp? st=wi&ct=2 (accessed January 4, 2001)
  • 2.Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann; 1988. [Google Scholar]
  • 3.Andreassen S, Jensen FV, Olesen KG. Medical expert systems based on causal probabilistic networks. Int J Biomed Comput. 1991;28:1–30. doi: 10.1016/0020-7101(91)90023-8. [DOI] [PubMed] [Google Scholar]
  • 4.Tombropoulos R, Shiffman S, Davidson C: A decision aid for diagnosis of liver lesions on MRI. Proc Annu Symp Comput Appl Med Care, 1993, pp 439–443 [PMC free article] [PubMed]
  • 5.Haddawy P, Kahn CE, Butarbutar M. A Bayesian network model for radiological diagnosis and procedure selection: Work-up of suspected gallbladder disease. Med Phys. 1994;21:1185–1192. doi: 10.1118/1.597400. [DOI] [PubMed] [Google Scholar]
  • 6.Kahn CE Jr, Roberts LM, Wang K, et al: Preliminary investigation of a Bayesian network for mammographie diagnosis of breast cancer. Proc Annu Symp Comput Appl Med Care, 1995, pp 208–212 [PMC free article] [PubMed]
  • 7.Bumside E, Rubin D, Shachter R: A Bayesian network for mammography. Proc AMIA Annu Fall Symp, 2000, pp 106–110 [PMC free article] [PubMed]
  • 8.Lodwick GS. A probabilistic approach to the diagnosis of bone tumors. Radiol Clin North Am. 1965;3:487–497. [PubMed] [Google Scholar]
  • 9.Dahlin DC: Bone Tumors: General Aspects and Data on 8,542 Cases (ed 14). Rochester, MN, Mayo Foundation, 1986
  • 10.Hudson TM. Radiologie-Pathologie Correlation of Musculoskeletal Lesions. Baltimore, MD: Williams & Wilkins; 1987. [Google Scholar]
  • 11.Lodwick GS. The Bones and Joints: An Atlas of Tumor Radiology. Chicago, IL: American College of Radiology; 1971. [Google Scholar]
  • 12.Haddawy P, Jacobson J, Kahn CE. A Bayesian network tutoring shell. Artif Intell Med. 1997;10:177–200. doi: 10.1016/S0933-3657(96)00374-0. [DOI] [PubMed] [Google Scholar]

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