The Role of Muscle Biopsy in the Diagnosis of Inflammatory Myopathy

Muscle biopsies of patients with immune-mediated inflammatory myopathies are characterized by a combination of chronic inflammatory infiltrates and muscle fiber necrosis. In addition, there are typical histochemical, immunohistochemical, and ultrastructural findings of each type of inflammatory myopathy. Accurate classification of inflammatory myopathies is important because inclusion body myositis does not respond to immunosuppressive treatment, polymyositis is frequently part of an overlap syndrome, and dermatomyositis is associated with cancer in adults. Necrotizing myopathy without inflammation may be due to a partially treated inflammatory myopathy, a toxic myopathy, or a paraneoplastic myopathy. Metabolic myopathies and muscular dystrophies may clinically and rarely pathologically mimic inflammatory myopathy. Correct identification of these entities depends on better awareness of clinical similarities between metabolic myopathy and inflammatory myopathy; appropriate utilization of immunohistochemical, ultrastructural, biochemical, and genetic techniques on muscle samples in difficult cases; and maximizing communication between the clinician and the pathologist

Glioneuronal Neoplasms with Malignant Histological Features: A Study of 36 Cases

Objective: Malignant glioneuronal tumors show considerable morphological diversity. Their biological behavior and clinicopathological characteristics are incompletely understood. With the exception of anaplastic ganglioglioma, they are not assigned to a specific entity in the current WHO classification. It is also not clear whether histological features of these neoplasms influence prognosis. Material and Method: We identified 36 glioneuronal tumors with malignant histological features among the departmental archives and neuropathology consultation files of the authors. We reviewed the pathological and radiological features of these tumors to construct a preliminary histological categorization. Results: Based on their pathological features, we divided the study group into three histologically distinct categories: 1) glioneuronal tumors with a malignant glial component (anaplastic gangliogliomas); 2) glioneuronal tumors with a malignant neuronal/neuroblastic component; 3) glioneuronal tumors with both malignant neuronal and glial components. All tumors occurred in a younger age group compared to glioblastomas and appeared radiologically well-defined, cystic and solid with variable contrast enhancement. There was a high rate of local recurrence (29 of 36 patients) and 12 patients died during follow-up period. Median progression-free survival was less than 12 months, and did not differ among categories. Cerebrospinal tumor spread was seen in only one patient. Concurrent WHO grade I ganglioglioma and the presence of a malignant neuronal component did not appear to influence prognosis Conclusion: MGNTs were considered in three simple categories based on their malignant component(s). Tumors in all categories exhibited a high rate of local recurrence and aggressive behavior akin to malignant gliomas as opposed to classical PNET. Nevertheless, MGNT demonstrated clinicopathological features that distinguish them from typical glioblastoma. The exact nosology of MGNTs is unresolved and our study underscores the need for a more comprehensive classification of these neoplasms within the WHO scheme

The cell-graphs of cancer

We report a novel, proof-of-concept, computational method that models a type of brain cancer (glioma) only by using the topological properties of its cells in the tissue image. From low-magni cation (80X) tissue images of 384x384 pixels, we construct the graphs of the cells based on the locations of the cells within the images. We generate such cell-graphs of 1,000-3,000 cells (nodes) with 2,000-10,000 links, each of which is calculated as a decaying exponential function of the Euclidean distance between every pair of cells in accordance with the Waxman model. At the cellular level, we compute the graph metrics of the cell-graphs including the degree, clustering coef cient, eccentricity, and closeness for each cell. Working with a total of 285 tissue samples surgically removed from 12 different patients, we demonstrate that the self-organizing clusters of cancerous cells exhibit distinctive graph metrics that distinguish them from the healthy cells and the unhealthy in amed cells at the cellular level with an accuracy of at least 85%. At the tissue level, we accomplish correct tissue classi cations of cancerous, healthy, and nonneoplastic in amed tissue samples with an accuracy of 100% by requiring correct classi cation for the majority of the cells within the tissue sample

Augmented cell-graphs for automated cancer diagnosis

This work reports a novel computational method based on Augmented Cell Graphs (ACG), which are constructed from lowmagnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted, and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both the nodes and the edges of an ACG are assigned weights to capture more information about the topology of the tissue. In this work, the experiments are conducted on a data set that comprised of 646 human brain biopsy samples from 60 different patients. It is shown that the ACG approach yields sensitivity of 97.53% and specificities of 93.33 % and 98.15 % (for the inflamed and healthy, respectively) at the tissue level in glioma diagnosis

Learning the Topological Properties of Brain Tumors

Different types of feature representation have been investigated to represent the histopathological images for the purpose of cancer diagnosis. In this work, we demonstrate that cell-graphs provide effective representations as they encode the pairwise relation between every cell by statistically assigning a link between them. Working with photomicrographs of 646 archival brain biopsy samples from 60 patients, we show that without this pairwise relation, neither the spatial distribution of the cells nor the texture analysis of the images yields as accurate results as in the case of the cell graphs to distinguish cancerous tissues from non-cancerous tissues with similar cellular density levels. We use the global graph metrics that are defined on the entire cell-graph as a feature set of a multilayer perceptron for the tissue level diagnosis of a brain cancer called malignant glioma. In our experiments, we correctly classify the cancerous and healthy brain tissue samples that have significantly different cellular density levels with accuracy greater than 99 %. Furthermore, we accomplish distinguishing the cancerous tissues from non-neoplastic reactive/inflammatory conditions that may reveal an equally high cellular density; with an accuracy of at least 92 %