Removal of a Metallic Stent after 9 Years of Placement That Caused Tracheal Stenosis: A Rare Case Report

Introduction: Metallic stents are widely used to prevent airway obstruction for tracheal stenosis caused by malignant diseases. Although their efficacy has been recognized, there is no established evidence surrounding their long-term safety. We report a case of airway stenosis caused by a metallic tracheal stent. Removal of the stent to secure the airway was difficult and extremely complicated. Case Presentation: A 50-year-old male suffering from dyspnea caused by malignant lymphoma (diffuse large B-cell lymphoma) of the thyroid gland was treated with a metallic tracheal stent. After remission of the lymphoma, stenosis of the stent lumen developed gradually, and the patient complained of dyspnea. Tracheostomy could not be performed due to the metallic stent. Since the patient was unable to intubate, the stent was removed under general anesthesia with partial percutaneous cardiopulmonary support 9 years after the stent placement. Conclusion: Otolaryngologists should be aware of the possibility of severe stenosis following the long-term placement of a metallic tracheal stent

phyC: Clustering cancer evolutionary trees

<div><p>Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from <a href="https://github.com/ymatts/phyC" target="_blank">https://github.com/ymatts/phyC</a>.</p></div

Results of the simulations.

<p>Each row of panels represents the simulation type (simulation I, II, and III), and each column represents the external clustering validation indices: purity (PR), normalized mutual information (NMI), and Rand index (RI). The horizontal axis of each graph is the variance parameter defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005509#pcbi.1005509.s001" target="_blank">S1 Text</a>, and the vertical axis is the external validation index. The bold lines and the bands indicate the mean and 95% confidence interval of the index for 100 replicates of each dataset.</p

Clustering result of the ccRCC and NSCLC datasets.

<p>Clustering result of the ccRCC and NSCLC datasets.</p

Classes of cancer evolutionary trees in the simulations.

<p><b>Simulation I</b>: Five classes of tree topologies were considered: monoclonal (MC), polyclonal-low (PL), polyclonal-middle (PM), polyclonal-high (PH), and mutator-phenotype (MT). <b>Simulation II</b>: Three classes of edge lengths of the tree are considered: trunk accumulation (TR), branched accumulation (BR), and balanced accumulation (BL). <b>Simulation III</b>: Nine classes of trees are considered: polyclonal-low trunk accumulation (PL-TR), polyclonal-low balanced accumulation (PL-BL), polyclonal-low branch accumulation (PL-BR), polyclonal-middle trunk-accumulation (PM-TR), polyclonal-middle balanced-accumulation (PM-BL), polyclonal-middle branch-accumulation (PM-BR), polyclonal-high trunk accumulation (PH-TR), polyclonal-high balanced accumulation (PH-BL), and polyclonal-high branch accumulation (PH-BR)</p

Clustering result of the NSCLC dataset.

<p>Clustering result of the NSCLC dataset.</p

Overview of the proposed method.

<p>(A) Example of cancer evolution. A founder cell is established after a normal cell acquires several passenger mutations and driver mutations (founder SSNVs), and sub-clones evolve by acquiring progressor SSNVs. Each color (purple, orange, dark blue, light blue, and green) of circles represents different sub-clones. (B) Example of a cancer evolutionary tree in the case of (A). A root and its immediate node represent the normal cell and founder cell, respectively. Subsequent nodes indicate sub-clones and edge lengths indicate the number of SSNVs acquired in the sub-clones. (C) Example of the registration of a tree. To resolve (p1)–(p4) for comparison of the evolutionary trees, a sufficiently large bifurcated tree is constructed, which is the reference tree (note that we have omitted bifurcation from the root for clearer visualization). The tree topologies and attributes are mapped to the reference tree beginning with those with the largest depths to those with the smallest depths. In the case of a tie, the sub-trees are mapped from those with the largest edge lengths. Zero-length edges are regarded as degenerated edges (dashed lines). Edge lengths are normalized by the sum of all edge lengths within tumors. The resulting trees can be represented as edge length vectors <b>z</b>_<i>i</i>. (D) Clustering cancer evolutionary trees to summarize the evolutionary history of cancer for each patient. The trees are reconstructed based on the VAFs and then <i>n</i> cancer sub-clonal evolutionary trees are divided into <i>K</i> subgroups based on tree topologies and edge attributes. Through the registration, <i>n</i> evolutionary trees can be represented as <i>m</i>-dimensional <i>n</i> vectors in Euclidean space, and a standard clustering algorithm can be applied.</p

Clustering result of the ccRCC dataset.

<p>Clustering result of the ccRCC dataset.</p