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  1. CNS germinoma with extensive calcification: An unusual histologic finding
    Tan GC, Sallapan S, Haworth K, Finlay J, Boue DR, Pierson CR
    Malays J Pathol, 2019 Apr;41(1):71-73.
    PMID: 31025642
    INTRODUCTION: Intratumoral calcification is a feature that is more often observed in pineal parenchymal tumour than germinoma. We describe a 13-year-old male with pineal region germinoma demonstrating extensive intratumoral calcification.

    CASE REPORT: He presented with worsening headache that was associated with fatigue, nausea and vomiting. Radiologic examination revealed a multilobular mass in the pineal region with internal calcifications. Biopsy showed a pure germinoma with unusually extensive calcification.

    DISCUSSION: Although a diagnosis may be suggested with a careful evaluation of imaging, there is no pathognomonic pattern. Thus, histologic verification is necessary for most pineal region masses.

  2. Fulltext A multi-resolution textural approach to diagnostic neuropathology reporting
    Fauzi MF, Gokozan HN, Elder B, Puduvalli VK, Pierson CR, Otero JJ, et al.
    J Neurooncol, 2015 Sep;124(3):393-402.
    PMID: 26255070 DOI: 10.1007/s11060-015-1872-4
    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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