Cystic changes in metastatic cervical lymph nodes (CLN) from papillary thyroid cancer (PTC) may be a diagnostic pitfall in fine-needle aspiration biopsy (FNAB) cytology. We investigated in a series of CLN metastases from thyroid cancers (TC), including cystic PTC, and from a wide spectrum of extrathyroidal malignancies, the diagnostic role for metastatic TC of the rapid detection of thyroglobulin in eluates from FNAB (FNAB-Tg) of CLN. The study was carried out in a group of 79 subjects (22/57 M/F; median age, 56 years; range, 20-86 years) with enlarged CLN and thyroid nodules (TN), examined for potential metastatic TC, and harboring a large spectrum of incidentally diagnosed extrathyroidal malignancies (n = 24, mostly represented by lymphomas, lung, and breast cancers), CLN metastases from thyroid cancers (n = 28, including 6 cystic metastatic PTC), 6 specific lymphadenitis and 21 reactive lymphadenitis mostly detected (n = 16) during follow-up of patients with previously ablated TC. Markedly high FNAB thyroglobulin (Tg) values were found in all metastatic CLN TC. Two of the six cases with cystic metastatic CLN PTC were diagnosed by FNAB-Tg but not by cytology. In conclusion, FNAB-Tg has been confirmed as an easy modality and fast procedure to diagnose CLN metastasis from TC and high FNAB-Tg values with nondiagnostic cystic cytology strongly suggest cystic metastatic PTC.

International audienceThe (Gromov) hyperbolicity is a topological property of a graph, which has been recently applied in several different contexts, such as the design of routing schemes, network security, computational biology, the analysis of graph algorithms, and the classification of complex networks. Computing the hyperbolicity of a graph can be very time consuming: indeed, the best available algorithm has running-time O(n^{3.69}), which is clearly prohibitive for big graphs. In this paper, we provide a new and more efficient algorithm: although its worst-case complexity is O(n^4), in practice it is much faster, allowing, for the first time, the computation of the hyperbolicity of graphs with up to 200,000 nodes. We experimentally show that our new algorithm drastically outperforms the best previously available algorithms, by analyzing a big dataset of real-world networks. Finally, we apply the new algorithm to compute the hyperbolicity of random graphs generated with the Erdös-Renyi model, the Chung-Lu model, and the Configuration Model

Centrality indices are widely used analytic measures for the importance of nodes in a network. Closeness centrality is very popular among these measures. For a single node v, it takes the sum of the distances of v to all other nodes into account. The currently best algorithms in practical applications for computing the closeness for all nodes exactly in unweighted graphs are based on breadth-first search (BFS) from every node. Thus, even for sparse graphs, these algorithms require quadratic running time in the worst case, which is prohibitive for large networks.In many relevant applications, however, it is unnecessary to compute closeness values for all nodes. Instead, one requires only the k nodes with the highest closeness values in descending order. Thus, we present a new algorithm for computing this top-k ranking in unweighted graphs. Following the rationale of previous work, our algorithm significantly reduces the number of traversed edges. It does so by computing upper bounds on the closeness and stopping the current BFS search when k nodes already have higher closeness than the bounds computed for the other nodes.In our experiments with real-world and synthetic instances of various types, one of these new bounds is good for small-world graphs with low diameter (such as social networks), while the other one excels for graphs with high diameter (such as road networks). Combining them yields an algorithm that is faster than the state of the art for top-k computations for all test instances, by a wide margin for high-diameter * E. B.'s and H. M.

Search results clustering (SRC) is a challenging algorithmic problem that requires grouping together the results returned by one or more search engines in topically coherent clusters, and labeling the clusters with meaningful phrases describing the topics of the results included in them.In this paper we propose to solve SRC via an innovative approach that consists of modeling the problem as the labeled clustering of the nodes of a newly introduced graph of topics. The topics are Wikipedia-pages identified by means of recently proposed topic annotators [9,11,16,20] applied to the search results, and the edges denote the relatedness among these topics computed by taking into account the linkage of the Wikipedia-graph.We tackle this problem by designing a novel algorithm that exploits the spectral properties and the labels of that graph of topics. We show the superiority of our approach with respect to academic state-of-the-art work [6] and wellknown commercial systems (Clusty and Lingo3G) by performing an extensive set of experiments on standard datasets and user studies via Amazon Mechanical Turk. We test several standard measures for evaluating the performance of all systems and show a relative improvement of up to 20%.

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International audienceWe propose a new algorithm for the classical problem of computing the diameter of undirected unweighted graphs, namely, the maximum distance among all the pairs of nodes, where the distance of a pair of nodes is the number of edges contained in the shortest path connecting these two nodes. Although its worst-case complexity is O(nm) time, where n is the number of nodes and m is the number of edges of the graph, we experimentally show that our algorithm works in O(m) time in practice, requiring few breadth-first searches to complete its task on almost 200 real-world graphs

The clinical and diagnostic findings of 3 cases of papillary thyroid carcinoma in thyroglossal duct cyst (TDC) were compared to those of 3 cases of adenoma in TDC and 2 cases of benign TDC. The neck masses of the subjects with benign TDC grew slowly, whereas those of 2 patients with papillary carcinoma and 1 of the patients with adenoma grew rapidly (especially those with carcinoma). On the other hand, one case of carcinoma, and two cases of adenoma in TDC were diagnosed incidentally. Benign TDC had an anechoic pattern at US, whereas the cysts containing carcinoma and adenoma showed the presence of a mural nodule at US. Microcalcifications in the mural mass were present in one patient with carcinoma. The 3 patients with carcinoma in TDC underwent total thyroidectomy. The histology was negative in all 3 patients for thyroid cancer and thyroid nodules. However, in 2 of them it revealed the carcinoma invading the cyst wall and adjacent tissues, 1 of which also exhibited 2 metastatic lymph nodes in the central neck area. The cases reported illustrate the utility of enhancing one's clinical suspicion of carcinoma in patients bearing TDC, even when incidentally discovered. In particular, rapid growth of the cystic mass, and the presence of a mural nodule on US, especially with calcifications, must raise the physician's suspicion for a cancer arising in TDC.

Algorithms for listing the subgraphs satisfying a given property (e.g., being a clique, a cut, a cycle, etc.) fall within the general framework of set systems. A set system (U, F) uses a ground set U (e.g., the network nodes) and an indicator F ⊆ 2 U of which subsets of U have the required property. For the problem of listing all sets in F maximal under inclusion, the ambitious goal is to cover a large class of set systems, preserving at the same time the efficiency of the enumeration. Among the existing algorithms, the best-known ones list the maximal subsets in time proportional to their number but may require exponential space. In this paper we improve the state of the art in two directions by introducing an algorithmic framework that, under standard suitable conditions, simultaneously (i) extends the class of problems that can be solved efficiently to strongly accessible set systems, and (ii) reduces the additional space usage from exponential in |U| to stateless, thus accounting for just O(q) space, where q ≤ |U| is the largest size of a maximal set in F.