Lymphoma

[Study of Skip Metastasis to Lateral Lymph Nodes in Low Rectal Cancer].

We reviewed the clinical records of 302 patients with low rectal carcinoma to evaluate the effectiveness of lateral lymph node(LLN)dissection in cases of skip metastases to the LLN. Patients were divided into 4 groups according to nodal status: group N(133 cases), no metastasis in both the mesorectum lymph node(MLN)and LLN; groupM(100 cases), metastasis in the MLN and no LLN metastasis; group L(15...

Deep convolutional neural network for reduction of contrast-enhanced region on CT images.

This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The transverse images were divided into 64 × 64 pixels. This resulted in 14 723 patches in total for both non-contrast and contrast-enhanced CT image pairs. Th...

Prediction of Clinical Events in Hemodialysis Patients Using an Artificial Neural Network.

Advanced chronic kidney disease (CKD) requires routine renal replacement therapy (RRT) that involves hemodialysis (HD) which may cause increased risk ...

Improved Imaging Resolution of Electrical Impedance Tomography Using Artificial Neural Networks for Image Reconstruction.

Electrical impedance tomography (EIT) is a noninvasive and non-radiative medical imaging technique based on detecting the inhomogeneous electrical pro...

Predicting lymphoma outcomes and risk factors in patients with primary Sjögren's Syndrome using gradient boosting tree ensembles.

Primary Sjogren's Syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction, where it has been long stated that 5% of pSS ...

Validity of Natural Language Processing for Ascertainment of and Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer.

PURPOSE: SEER registries do not report results of epidermal growth factor receptor () and anaplastic lymphoma kinase () mutation tests. To facilitate ...

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentat...

GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.

For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agon...

The application of artificial neural networks (ANN) for the denaturation of meat proteins – the kinetic analysis method.

BACKGROUND: Artificial neural networks (ANN) are a common mathematical tool widely used in many research fields. Since they are applicable to non-line...

Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory ...

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.

The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI d...

Optimizing Outcome Prediction in Diffuse Large B-Cell Lymphoma by Use of Machine Learning and Nationwide Lymphoma Registries: A Nordic Lymphoma Group Study.

PURPOSE: Prognostic models for diffuse large B-cell lymphoma (DLBCL), such as the International Prognostic Index (IPI) are widely used in clinical pra...

[Effect of Brucea javanica oil emulsion on proliferation, migration and autophagy of non-small cell lung cancer A549 cells and the underlying mechanisms].

To investigate the effect of Brucea javanica oil emulsion on proliferation, migration and autophagy of non-small cell lung cancer A549 cells.
 Methods...

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.

OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They...

[Big data and artificial intelligence for diagnostic decision support in atypical dementia].

The differential diagnosis of atypical dementia remains difficult. The use of positron emission tomography (PET) still represents the gold standard fo...

Towards the Establishment of a Biomedical Ontology for the Primary Sjögren's Syndrome.

Primary Sjögren's Syndrome (pSS) has been characterized as a hypersensitivity reaction type II systemic autoimmune chronic disease causing exocrine gl...

DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.

The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifyin...

Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting.

Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework us...

Plasma fatty acid profile as biomarker of coronary artery disease: a pilot study using fourth generation artificial neural networks.

Many studies, focused on identifying new biomarkers for coronary artery disease (CAD) risk computation and monitoring, suggested a potential diagnosti...

Predictive Factors for Post-Ischemic Compartment Syndrome in Non-Traumatic Acute Limb Ischemia in a Lower Extremity.

: Compartment syndrome (CS) is serious complication following revascularization of acute limb ischemia (ALI). However, predictive factors associated w...