The incidence of CNS lymphoma has increased significantly in the past 30 years, primarily in the elderly and immunocompromised. While T-cell lymphomas comprise 15-20% of systemic lymphomas, they comprise less than 4% of primary CNS lymphomas, suggesting that they may be under-recognized compared to their systemic counterparts. To investigate this, we studied brain biopsies from three patients who were diagnosed with T-cell lymphoma confined to the brain. They had enhancing lesions by MRI, arising in the cerebellum and brainstem in one and temporal lobe in two. We compared these to biopsies from three patients who had reactive lymphoid infiltrates and who had clinical signs/symptoms and radiographic findings that were indistinguishable from the lymphoma group. Biopsies from both the lymphoma group and reactive group showed considerable cytomorphologic heterogeneity. Although one lymphoma case contained large atypical cells, the other two contained small, mature lymphocytes within a heterogeneous infiltrate of neoplastic and reactive inflammatory cells. Surface marker aberrancies were present in two lymphoma cases, but this alone could not reliably diagnose T-cell lymphoma. The proliferation index was not useful for differentiating lymphoma from reactive infiltrates. In five of the six cases the diagnosis was most influenced by clonality studies for T-cell receptor-gamma gene rearrangements. We conclude that because of the high degree of overlap in cytomorphologic and immunophenotypic features between T-cell lymphoma and reactive infiltrates, T-cell lymphoma may not be recognized unless studies for T-cell receptor gene rearrangements are performed for CNS lesions composed of a polymorphous but predominantly T-cell infiltrate.

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The retail industry across the world is realizing that delivering high levels of service quality and achieving customer satisfaction is the key for a sustainable competitive advantage. Researchers have found positive relations between retail service quality dimensions and customer satisfaction. Identifying and classifying the retail customers as 'satisfied' or 'dissatisfied' according to the retail service quality dimensions would be useful to retailers in enabling strategic decision making in a competitive and dynamic environment. Retailers generate and collect a huge amount of customer data on daily transactions, customer-shopping history, goods transportation, consumption patterns, and service records in a relatively short period. The explosive growth of data requires a more efficient way to extract useful knowledge which can help the retailers to make better business decisions and to target customers who might be profitable to them. The concept of data mining has emerged as an effective technique for exploring large amounts of data to discover meaningful patterns and rules in various fields including retail. In this paper, the retail customers are classified into either 'satisfied' or 'dissatisfied' classes according to the retail service quality dimensions. The research presents a comparative study of popular classification techniques such as decision tree classifier and support vector machine using the R-studio software. The paper uses machine learning algorithms to assess the Indian retail service quality. The results would help the retail organizations to enhance their overall service quality and to target their marketing efforts at the right group of customers.

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