Incidence of lymph node metastases in clinical early-stage mucinous and seromucinous ovarian carcinoma: a retrospective cohort study

Objective: The use of lymph node sampling during staging procedures in clinical early-stage mucinous ovarian carcinoma (MOC) is an ongoing matter of debate. Furthermore, the incidence of lymph node metastases (LNM) in MOC in relation to tumour grade (G) is unknown. We aimed to determine the incidence of LNM in clinical early-stage MOC per tumour grade. Design: Retrospective study with data from the Dutch Pathology Registry (PALGA). Setting: The Netherlands, 2002–2012. Population or sample: Patients with MOC. Methods: Histology reports on patients with MOC diagnosed in the Netherlands between 2002 and 2012 were obtained from PALGA. Reports were reviewed for diagnosis, tumour grade and presence of LNM. Clinical data, surgery reports and radiology reports of patients with LNM were retrieved from hospital files. Main outcome measures: Incidence of LNM, disease-free survival (DFS). Results: Of 915 patients with MOC, 426 underwent lymph node sampling. Cytoreductive surgery was performed in 267 patients. The other 222 patients received staging without lymph node sampling. In eight of 426 patients, LNM were discovered by sampling. In four of 190 (2.1%) patients with G1 MOC, LNM were present, compared with one of 115 (0.9%) patients with G2 MOC and three of 22 (13.6%) patients with G3 MOC. Tumour grade was not specified in 99 patients. Patients with clinical early-stage MOC had no DFS benefit from lymph node sampling. Conclusions: LNM are rare in early-stage G1 and G2 MOC without clinical suspicion of LNM. Therefore, lymph node sampling can be omitted in these patients

Node Sampling using Random Centrifugal Walks

Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that nodes are selected by a special node, called the \emph{source}, with a given probability distribution. All these algorithms are based on a new class of random walks, that we call Random Centrifugal Walks (RCW). A RCW is a random walk that starts at the source and always moves away from it. Firstly, an algorithm to sample any connected network using RCW is proposed. The algorithm assumes that each node has a weight, so that the sampling process must select a node with a probability proportional to its weight. This algorithm requires a preprocessing phase before the sampling of nodes. In particular, a minimum diameter spanning tree (MDST) is created in the network, and then nodes' weights are efficiently aggregated using the tree. The good news are that the preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. After that, every sample is done with a RCW whose length is bounded by the network diameter. Secondly, RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source. The key features of the RCW algorithms (unlike previous Markovian approaches) are that (1) they do not need to warm-up (stabilize), (2) the sampling always finishes in a number of hops bounded by the network diameter, and (3) it selects a node with the exact probability distribution

Endometrial Stromal Sarcoma Presenting As Puberty Menorrhagia

Endometrial stromal sarcomas are rare uterine tumours usually seen in perimenopausal females. We report here a case of low grade malignant endometrial stromal sarcoma in an adolescent girl, presenting as puberty menorrhagia. She underwent total hysterectomy with bilateral salpingo-oophorectomy and pelvic node sampling. She also received adjuvant chemotherapy and radiotherapy. She is disease free at completion of one year of follow-up

Hierarchical Graph Transformer with Adaptive Node Sampling

The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not achieved competitive performance, especially on large graphs. In this paper, we identify the main deficiencies of current graph transformers:(1) Existing node sampling strategies in Graph Transformers are agnostic to the graph characteristics and the training process. (2) Most sampling strategies only focus on local neighbors and neglect the long-range dependencies in the graph. We conduct experimental investigations on synthetic datasets to show that existing sampling strategies are sub-optimal. To tackle the aforementioned problems, we formulate the optimization strategies of node sampling in Graph Transformer as an adversary bandit problem, where the rewards are related to the attention weights and can vary in the training procedure. Meanwhile, we propose a hierarchical attention scheme with graph coarsening to capture the long-range interactions while reducing computational complexity. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of our method over existing graph transformers and popular GNNs.Comment: Accepted by NeurIPS 202

Scalable and Robust Community Detection with Randomized Sketching

This paper explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a reduced graph sketch constructed using random sampling. Then, the clusters of the full graph are inferred based on the clusters extracted from the sketch using a correlation-based retrieval step. Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced. A new random degree-based node sampling algorithm is presented which significantly improves upon the performance of the clustering algorithm even when clusters are unbalanced. This algorithm improves the phase transitions for matrix-decomposition-based clustering with regard to computational complexity and minimum cluster size, which are shown to be nearly dimension-free in the low inter-cluster connectivity regime. A third sampling technique is shown to improve balance by randomly sampling nodes based on spatial distribution. We provide analysis and numerical results using a convex clustering algorithm based on matrix completion

Brief Announcement: Node Sampling Using Centrifugal Random Walks.

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter

Image directed lymph node sampling for lung cancer staging

http://deepblue.lib.umich.edu/bitstream/2027.42/117374/1/40644_2014_Article_102.pd

Uniform Node Sampling Service Robust against Collusions of Malicious Nodes

International audienceWe consider the problem of achieving uniform node sampling in large scale systems in presence of a strong adversary. We first propose an omniscient strategy that processes on the fly an unbounded and arbitrarily biased input stream made of node identifiers exchanged within the system, and outputs a stream that preserves Uniformity and Freshness properties. We show through Markov chains analysis that both properties hold despite any arbitrary bias introduced by the adversary. We then propose a knowledge-free strategy and show through extensive simulations that this strategy accurately approximates the omniscient one. We also evaluate its resilience against a strong adversary by studying two representative attacks (flooding and targeted attacks). We quantify the minimum number of identifiers that the adversary must insert in the input stream to prevent uniformity. To our knowledge, such an analysis has never been proposed before