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Prognostic Reporting of p53 Expression by Image Analysis in Glioblastoma Patients: Detection and Classification

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Health Information Science (HIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9085))

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Abstract

In this paper, we present a computer aided diagnosis system focusing on one important diagnostic branchpoint in clinical decision-making: prognostic reporting of p53 expression in glioblastoma patients. Studies in other tumor paradigms have shown that the staining intensity correlates with TP53 mutation status, and that gliomas show inter-tumoral heterogeneity in p53 mutation status. Increasing diagnostic accuracy by computer-aided image analysis algorithms would deliver an objective assessment of such prognostic biomarkers. We proposed a method for the detection and classification of positive and negative cells in digitized p53-stained images by means of a novel adaptive thresholding for the detection, and two-step rule based on weighted color and intensity for the classification. The proposed thresholding technique is able to correctly locate both positive and negative cells by effectively addressing the closely connected cells problem, and records a promising 85% average precision and 88% average recall rate. On the other hand, the proposed two-step rule achieves 81% classification accuracy, which is comparable with neuropathologists’ markings.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia

    Mohammad F. Ahmad Fauzi

  2. Department of Pathology, The Ohio State University, Columbus, Ohio, USA

    Hamza N. Gokozan, Christopher R. Pierson & Jose J. Otero

  3. Department of Pathology and Laboratory Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USA

    Christopher R. Pierson

  4. Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA

    Metin N. Gurcan

Authors
  1. Mohammad F. Ahmad Fauzi
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  2. Hamza N. Gokozan
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  3. Christopher R. Pierson
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  4. Jose J. Otero
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  5. Metin N. Gurcan
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Corresponding author

Correspondence to Mohammad F. Ahmad Fauzi .

Editor information

Editors and Affiliations

  1. Victoria University, Melbourne, Australia

    Xiaoxia Yin

  2. Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada

    Kendall Ho

  3. University of Arizona, Tucson, Arizona, USA

    Daniel Zeng

  4. University of Nottingham, Nottingham, United Kingdom

    Uwe Aickelin

  5. Victoria University, Melbourne, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Australia

    Hua Wang

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© 2015 Springer International Publishing Switzerland

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Ahmad Fauzi, M.F., Gokozan, H.N., Pierson, C.R., Otero, J.J., Gurcan, M.N. (2015). Prognostic Reporting of p53 Expression by Image Analysis in Glioblastoma Patients: Detection and Classification. In: Yin, X., Ho, K., Zeng, D., Aickelin, U., Zhou, R., Wang, H. (eds) Health Information Science. HIS 2015. Lecture Notes in Computer Science(), vol 9085. Springer, Cham. https://doi.org/10.1007/978-3-319-19156-0_17

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  • DOI: https://doi.org/10.1007/978-3-319-19156-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19155-3

  • Online ISBN: 978-3-319-19156-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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