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A multi-resolution textural approach to diagnostic neuropathology reporting

  • Laboratory Investigation
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Abstract

We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists’ markings.

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Acknowledgments

Support for this research was provided by the Ohio State University Comprehensive Cancer Center using Pelotonia funds.

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    Authors and Affiliations

    1. Faculty of Engineering, Multimedia University, Jalan Multimedia, 63100, Cyberjaya, Selangor, Malaysia

      Mohammad Faizal Ahmad Fauzi

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

      Hamza Numan Gokozan, Christopher R. Pierson & José Javier Otero

    3. Department of Neurological Surgery, The Ohio State University, Columbus, OH, USA

      Brad Elder

    4. Division of Neuro-oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA

      Vinay K. Puduvalli

    5. Department of Pathology and Laboratory Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

      Christopher R. Pierson

    6. Division of Anatomy, The Ohio State University, Columbus, OH, USA

      Christopher R. Pierson

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

      Metin N. Gurcan

    8. Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, 4169 Graves Hall, 333 W 10th Avenue, Columbus, OH, 43210, USA

      José Javier Otero

    9. 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA

      Metin N. Gurcan

    10. 320-K Lincoln Tower, Columbus, USA

      Metin N. Gurcan

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    Mohammad Faizal Ahmad Fauzi and Hamza Numan Gokozan are first co-authors.

    Mohammad Faizal Ahmad Fauzi and Hamza Numan Gokozan have contributed equally to this work.

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    Fauzi, M.F.A., Gokozan, H.N., Elder, B. et al. A multi-resolution textural approach to diagnostic neuropathology reporting. J Neurooncol 124, 393–402 (2015). https://doi.org/10.1007/s11060-015-1872-4

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    • DOI: https://doi.org/10.1007/s11060-015-1872-4

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