WHO 2010 classification of pancreatic endocrine tumors. Is the new always better than the old?

Pancreatology ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 539-541 ◽  
Author(s):  
Claudio Ricci ◽  
Riccardo Casadei ◽  
Giovanni Taffurelli ◽  
Marielda D'Ambra ◽  
Francesco Monari ◽  
...  
Keyword(s):  
Endocrine Tumors ◽  
Pancreatic Endocrine Tumors ◽  
Who 2010 Classification ◽  
Better Than

Update on Pancreatic Endocrine Tumors

2006 ◽  
Vol 130 (7) ◽  
pp. 963-966 ◽  
Author(s):  
Wendy L. Frankel
Keyword(s):  
Mass Effect ◽  
Acinar Cell Carcinoma ◽  
Ductal Adenocarcinoma ◽  
Solid Pseudopapillary Tumor ◽  
Endocrine Tumors ◽  
Term Survival ◽  
Pancreatic Endocrine Tumors ◽  
Adjacent Structures

Abstract Endocrine tumors of the pancreas represent 1% to 2% of all pancreatic neoplasms. The tumors tend to have an indolent behavior, and long-term survival is common. There is no gender or age predilection. Patients can present with symptoms due to hormonal excess or a local mass effect or be asymptomatic. The tumors tend to be solid and well circumscribed. Typical microscopic findings include an organoid pattern of growth, with cells containing scant to moderate amounts of cytoplasm, and nuclei with dispersed chromatin and inconspicuous nucleoli. The morphologic spectrum of these tumors can be variable, and the differential diagnosis includes chronic pancreatitis with neuroendocrine hyperplasia, ductal adenocarcinoma, solid pseudopapillary tumor, acinar cell carcinoma, and pancreatoblastoma. The classification of these tumors remains controversial, and prognosis is difficult to predict, but important features include metastasis and invasion of adjacent structures. Resection remains the mainstay of surgical treatment. It is important to be aware that unusual morphologic variants of pancreatic endocrine tumors are common, and immunohistochemical stains can help avoid misdiagnosis.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

A Prognostic Clinicopathologic Classification of Pituitary Endocrine Tumors

2015 ◽  
Vol 44 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Gérald Raverot ◽  
Alexandre Vasiljevic ◽  
Emmanuel Jouanneau ◽  
Jacqueline Trouillas
Keyword(s):  
Endocrine Tumors

Nonfunctioning pancreatic endocrine tumors: a multicenter clinical study

2003 ◽  
Vol 98 (11) ◽  
pp. 2435-2439 ◽  
Author(s):  
Lucio Gullo ◽  
Marina Migliori ◽  
Massimo Falconi ◽  
Paolo Pederzoli ◽  
Rossella Bettini ◽  
...  
Keyword(s):  
Clinical Study ◽  
Endocrine Tumors ◽  
Pancreatic Endocrine Tumors ◽  
Multicenter Clinical Study

scholarly journals Pancreatic endocrine tumors

2011 ◽  
Vol 24 (S2) ◽  
pp. S66-S77 ◽  
Author(s):  
Sylvia L Asa
Keyword(s):  
Endocrine Tumors ◽  
Pancreatic Endocrine Tumors

Pancreatic Endocrine Tumors

2007 ◽  
Vol 36 (2) ◽  
pp. 431-439 ◽  
Author(s):  
Niraj Jani ◽  
A. James Moser ◽  
Asif Khalid
Keyword(s):  
Endocrine Tumors ◽  
Pancreatic Endocrine Tumors

scholarly journals Methylation-associated down-regulation of RASSF1A and up-regulation of RASSF1Cin pancreatic endocrine tumors

BMC Cancer ◽  
2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Giorgio Malpeli ◽  
Eliana Amato ◽  
Mario Dandrea ◽  
Caterina Fumagalli ◽  
Valentina Debattisti ◽  
...  
Keyword(s):  
Endocrine Tumors ◽  
Down Regulation ◽  
Pancreatic Endocrine Tumors

Improved histologic and clinicopathologic criteria for prognostic evaluation of pancreatic endocrine tumors

2009 ◽  
Vol 40 (1) ◽  
pp. 30-40 ◽  
Author(s):  
Stefano La Rosa ◽  
Catherine Klersy ◽  
Silvia Uccella ◽  
Linda Dainese ◽  
Luca Albarello ◽  
...  
Keyword(s):  
Endocrine Tumors ◽  
Prognostic Evaluation ◽  
Pancreatic Endocrine Tumors

Multiple primary malignancies in patients with sporadic pancreatic endocrine tumors

2008 ◽  
Vol 97 (7) ◽  
pp. 592-595 ◽  
Author(s):  
Volker Fendrich ◽  
Jens Waldmann ◽  
Detlef K. Bartsch ◽  
Katja Schlosser ◽  
Matthias Rothmund ◽  
...  
Keyword(s):  
Endocrine Tumors ◽  
Multiple Primary Malignancies ◽  
Pancreatic Endocrine Tumors ◽  
Multiple Primary
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