Oncology/Hematology

Association of Helicobacter pylori Infection With Colon Cancer and Adenomatous Polyps.

BACKGROUND AND OBJECTIVE: infection is one of the most common chronic bacterial infections in the world, especially in the developing countries. This bacterium is the cause of many diseases such as lymphoma, gastritis, peptic ulcers, and stomach cancer. According to recent reports, infection can potentially increase the risk of colon cancer. The current study aimed at investigating the associati...

HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders.

Recent dramatic advances in multiomics research coupled with exponentially increasing volume, complexity, and interdisciplinary nature of publications are making it challenging for scientists to stay up-to-date on the literature. Strategies to address this challenge include the creation of online databases and warehouses to support timely and targeted dissemination of research findings. Although m...

Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT.

BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The ...

AluScanCNV2: An R package for copy number variation calling and cancer risk prediction with next-generation sequencing data.

The usage of next-generation sequencing (NGS) to detect copy number variation (CNV) is widely accepted in cancer research. Based on an AluScanCNV soft...

Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm.

PURPOSE: This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab ...

Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver.

Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for...

EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot...

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer rele...

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

OBJECTIVE: To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for...

An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.

The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Ge...

Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks.

PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this...

The usefulness of fine-needle aspirates for detection of recurrent carcinoma in the thyroid bed.

INTRODUCTION: Locoregional recurrence of thyroid carcinoma has a negative impact on patient prognosis. In the current study, we retrospectively review...

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer.

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath...

Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model.

Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had alrea...

The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques.

PURPOSE: Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appeal...

Machine learning and modeling: Data, validation, communication challenges.

With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatme...

Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score.

BACKGROUND: Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet ...

Identification of synthetic lethality based on a functional network by using machine learning algorithms.

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve c...

Computational Optics Enables Breast Cancer Profiling in Point-of-Care Settings.

The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpe...

Prevalence of and factors related to anxiety and depression symptoms among married patients with gynecological malignancies in China.

OBJECTIVE: This study aims to investigate the prevalence of anxiety and depression among married patients with gynecological malignancies in China and...