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Dynamic Bayesian Network for Cervical Cancer Screening

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Foundations of Biomedical Knowledge Representation

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9521))

Abstract

In this chapter we will present the application of dynamic Bayesian networks to cervical cancer screening. The main goal of this project was to create a multivariate model that would incorporate several variables in one framework and predict the risk of developing cervical precancer and invasive cervical cancer. We were interested in identifying those women that are at higher risk of developing cervical cancer and that should be screened differently than indicated in the guidelines.

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Notes

  1. 1.

    World Health Organization (http://globocan.iarc.fr/), accessed on July, 2014.

  2. 2.

    The second author of this article is the expert of the PCCSM model.

  3. 3.

    The Bethesda classification is a system for reporting Pap test interpretations. It was developed during the American Society for Colposcopy and Cervical Pathology Consensus Conference that took place in Bethesda, MD, USA [19]. The main goal of this meeting was to establish a standardized terminology in cytology diagnostic reports.

  4. 4.

    The introduction to this part of the book contains a brief description of dynamic Bayesian networks with the examples presented in the GeNIe software.

  5. 5.

    CIN3+ stands for Cervical Intraepithelial Neoplasia grade 3 and indicates a severe dysplasia and worse including invasive cervical cancer.

  6. 6.

    ASCUS stands for Atypical Squamous Cells of Undetermined Significance and indicates mild cellular abnormality in the cervix.

  7. 7.

    CIN2 stands for Cervical Intraepithelial Neoplasia grade 2 and indicates moderate dysplasia that usually regresses.

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Acknowledgments.

We would like to thank Karen Lassige for her help in retrieving the data from the hospital database. We also acknowledge Magee-Womens Hospital cytology manager Nancy Mauser for her assistance in reviewing individual cytology reports and the lead cytotechnologist Jonee Matsko for her assistance in identifying cytology-histology correlates. Our study was approved by the Institutional Review Board, Magee-Womens Hospital, University of Pittsburgh (IRB#: PRO09070454).

Bayesian network models were created and tested using SMILE, an inference engine, and GeNIe, a development environment for reasoning in graphical probabilistic models, both developed at the Decision Systems Laboratory and available at https://dslpitt.org/genie/.

Author information

Authors and Affiliations

  1. Department of Pathology, Magee-Womens Hospital, University of Pittsburgh Medical Center, Pittsburgh, USA

    Agnieszka Onisko & R. Marshall Austin

  2. Faculty of Computer Science, Białystok University of Technology, Białystok, Poland

    Agnieszka Onisko

Authors
  1. Agnieszka Onisko
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  2. R. Marshall Austin
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Corresponding author

Correspondence to Agnieszka Onisko .

Editor information

Editors and Affiliations

  1. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, The Netherlands

    Arjen Hommersom

  2. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, Gelderland, The Netherlands

    Peter J.F. Lucas

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Onisko, A., Marshall Austin, R. (2015). Dynamic Bayesian Network for Cervical Cancer Screening. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-28007-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28006-6

  • Online ISBN: 978-3-319-28007-3

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