Oncology/Hematology

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics.

Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging problem and an active area of research. Particular challenges, similarly as in other segmentation problems, include the class-imbalance problem as well as confounding background in DCE-MR images. To address these issues, we propose a mask-guided hierarchical learning (MHL) framework f...

Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.

BACKGROUND AND AIMS: The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic a...

Knowledge-Based Planning for Identifying High-Risk Stereotactic Ablative Radiation Therapy Treatment Plans for Lung Tumors Larger Than 5 cm.

PURPOSE: Stereotactic ablative body radiation therapy (SABR) for lung tumors ≥5 cm can be associated with more toxicity than that for smaller tumors. ...

Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.

BACKGROUND: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from non...

Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been...

Artificial neural networks help to identify disease subsets and to predict lymphoma in primary Sjögren's syndrome.

OBJECTIVES: Primary Sjögren's syndrome (pSS) is a complex chronic systemic disorder, for which specific and effective therapeutic interventions are st...

Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.

OBJECTIVE:: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sen...

CeO Nanoparticles-Loaded pH-Responsive Microparticles with Antitumoral Properties as Therapeutic Modulators for Osteosarcoma.

Osteosarcoma is an aggressive form of bone cancer mostly affecting young people. To date, the most effective strategy for the treatment of osteosarcom...

Skin lesion classification with ensembles of deep convolutional neural networks.

Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin c...

Boosting support vector machines for cancer discrimination tasks.

Cancer is a complex disease that is caused by rapid alteration of genes. Prediction of the state of cancer in advance contributes to a better understa...

Establishment of a SVM classifier to predict recurrence of ovarian cancer.

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier...

Development of a Ready-to-Use Graphical Tool Based on Artificial Neural Network Classification: Application for the Prediction of Late Fecal Incontinence After Prostate Cancer Radiation Therapy.

PURPOSE: This study was designed to apply artificial neural network (ANN) classification methods for the prediction of late fecal incontinence (LFI) a...

Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method.

LncRNAs plays an important role in the regulation of gene expression. Identification of cancer-related lncRNAs GO terms and KEGG pathways is great hel...

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data.

This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal ...

Nonenzymatic Amperometric Aptamer Cytosensor for Ultrasensitive Detection of Circulating Tumor Cells and Dynamic Evaluation of Cell Surface N-Glycan Expression.

Dynamic assessment of glycan expression on the cell surface and accurate determination of circulating tumor cells are increasingly imperative for canc...

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathog...

Correlation of Prostate Cancer CHD1 Status with Response to Androgen Deprivation Therapy: a Pilot Study.

INTRODUCTION: CHD1 has been identified as a tumor suppressor gene in prostate cancer. Previous studies have shown strong associations between CHD1 del...

3-D Neural denoising for low-dose Coronary CT Angiography (CCTA).

CCTA has become an important tool for coronary arteries assessment in low and medium risk patients. However, it exposes the patient to significant rad...

Diagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-Analysis.

Objective: The aim of this study was to determine the diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation....

Distribution based Fuzzy Estimate Spectral Clustering for Cancer Detection with Protein Sequence and Structural Motifs.

Objective: In biological data analysis, protein sequence and structural motifs are an amino-acid sequence patterns that are widespread and used as too...