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Context.--Both salivary lymphadenomas (LADs) and Warthin-like mucoepidermoid carcinoma (MEC) contain components of epithelium and lymphoid stroma and their differential diagnosis can be difficult on the basis of morphology alone. Objective.--To clarify whether Warthin-like MEC was diagnosed as a LAD, and to compare their clinicopathologic features. Design.--A total of 16 LAD cases were analyzed for MAML2 rearrangement by using fluorescence in situ hybridization, and the clinical, histologic, immunohistochemical, and prognostic features were compared between MAML2 rearrangement-positive and MAML2 rearrangement-negative groups. Results.--Among the 16 cases investigated, 9 harbored a MAML2 rearrangement and were reclassified as Warthin-like MEC. The remaining 7 cases were classified as LADs with 1 nonsebaceous and 6 sebaceous cases. The patients with Warthin-like MEC had a wider age range (10-75 years) than the patients with LADs (36-68 years). Histologically, 2 of the 9 Warthin-like MECs (22.2%) showed focal invasion, whereas all the LADs had complete capsules. Warthin-like MECs exhibited a diverse epithelial cell morphology, including basaloid, glandular, cuboidal, epidermoid, with mucinous cells, although these cytologic features were seen only focally in some cases. Nonsebaceous LAD was composed of basaloid and glandular epithelial cells predominantly. In sebaceous LAD, the epithelial cells were composed of basaloid and large foamy sebaceous cells. In all cases, the stroma was composed mainly of lymphocytes accompanied by lymphoid follicles, although plasma cell infiltration could be much heavier in Warthin-like MEC. All the patients had a good outcome after a longer follow-up (3-166 months). Conclusions.--Warthin-like MEC can be misdiagnosed as a LAD owing to overlap in clinicopathologic features of the 2 entities. Careful histologic evaluation and detection of MAML2 rearrangement can facilitate their differential diagnosis. (Arch Pathol Lab Med. 2021;145:744-752; doi: 10.5858/arpa.2019-0702-OA, Lymphadenomas (LADs) are rare benign salivary gland tumors comprising epithelial cells, with or without sebaceous differentiation, intermixed with intense, reactive lymphoid proliferation. (1) Since their first description in 1960 by [...]

A spatial co-location pattern refers to a subset of spatial features whose instances are frequently co-located in spatial. In spatial data mining, most of the existing algorithms aim to discover the positive patterns. Moreover, there are patterns with strong negative correlations in spatial, such as negative co-location patterns. The discovery of such patterns is also greatly significant in some applications. Existing negative co-location patterns mining algori-thm is time-consuming and the number of mining results is huge. To address these problems, this paper explores the upward inclusion property of the negative co-location pattern, and proposes a minimal negative co-location pattern. Based on the minimal negative co-location patterns, all prevalent negative co-location patterns can be derived. In the negative co-location pattern mining process, the calculation of table instances of the candidate patterns is the funda-mental factor that restricts the mining efficiency. In order to reduce the calculation of the table instance effectively, three pruning strategies are proposed. A large number of experiments on real and synthetic data sets verify the correctness and efficiency of the proposed algorithm. In particular, the experimental results show that the minimal negative co-location patterns can compress the number of prevalent negative co-location patterns by more than 80%.

Malignant tumors are one of the serious diseases that endanger human health. The use of data mining techniques to mine the relationship between malignant tumors and various pathogenic factors has been attracted more and more attention. In practice, the relationship between tumor diseases and pathogenic factors is often fuzzy, and the occurrence of tumor diseases is not only affected by a single factor. However, there is no research to address the above problem. For this reason, the concept of fuzzy co-location patterns is proposed that combines the spatial co-location pattern mining with fuzzy theory, and pollution sources are fuzzified by the clustering method. Then, the method of extracting rules from the decision table is adopted to extract rules, and the corresponding confidence calculation algorithm is designed. As a result, a novel method which can discover the fuzzy relationships between tumor diseases and pathogenic factors is proposed. The effectiveness of the proposed method is verified on the actual case, and the effects of different parameters on the running time of the algorithm are analyzed by conducting experiments on synthetic datasets. At last, the theoretical analysis of the time complexity of the proposed algorithm is presented.