Oncology/Hematology

A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis.

Accurate prognosis is fundamental in planning an appropriate therapy for cancer patients. Consequent to the heterogeneity of the disease, intra- and inter-pathologist variability, and the inherent limitations of current pathological reporting systems, patient outcome varies considerably within similarly staged patient cohorts. This is particularly true when classifying stage II colorectal cancer p...

Optical classification of neoplastic colorectal polyps - a computer-assisted approach (the COACH study).

BACKGROUND AND AIMS: Clinical data suggest that the quality of optical diagnoses of colorectal polyps differs markedly among endoscopists. The aim of this study was to develop a computer program that was able to differentiate neoplastic from non-neoplastic polyps using unmagnified endoscopic pictures.

Feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy.

This study aimed to investigate the feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation fram...

Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network.

Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection of breast cancer is accomplished...

Comparative evaluation of cytotoxic, antimicrobial and antioxidant activities of the crude extracts of three species grown in Saudi Arabia.

Natural products from medicinal plants represent major resource of novel therapeutic substances for combating serious diseases including cancers and m...

Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.

Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to...

A Convolutional Neural Network Uses Microscopic Images to Differentiate between Mouse and Human Cell Lines and Their Radioresistant Clones.

: Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent technological advancement. Previously, several attempts h...

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies.

BACKGROUND: Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy iden...

Using Artificial Intelligence (Watson for Oncology) for Treatment Recommendations Amongst Chinese Patients with Lung Cancer: Feasibility Study.

BACKGROUND: Artificial intelligence (AI) is developing quickly in the medical field and can benefit both medical staff and patients. The clinical deci...

Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation.

The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and...

SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.

Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if det...

Organization of the cancer network in SUS: evolution of the care model.

In the current context of epidemiological transition, demographic changes, changes in consumption and lifestyle habits, and pressure on care costs and...

Enhanced Computational Model for Gravitational Search Optimized Echo State Neural Networks Based Oral Cancer Detection.

The Clinical Oncology of American Society report in 2016 predicted deaths are increased upto 9570 due to oral cancer. This cancer occurs due to abnorm...

Prostate Stereotactic Body Radiation Therapy With a Focal Simultaneous Integrated Boost: Acute Toxicity and Dosimetry Results From a Prospective Trial.

PURPOSE: This study aimed to report the early toxicity results of a prospective clinical trial of prostate stereotactic body radiation therapy (SBRT) ...

Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.

PURPOSE: Intensity modulated radiation therapy (IMRT) is commonly employed for treating head and neck (H&N) cancer with uniform tumor dose and conform...

PBN11-8, a Cytotoxic Polypeptide Purified from Marine , Suppresses Invasion and Migration of Human Hepatocellular Carcinoma Cells by Targeting Focal Adhesion Kinase Pathways.

The development of antitumor drugs has attracted cancer researchers and the identification of novel antitumor lead compounds is certainly of great int...

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other me...

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to di...

CGPS: A machine learning-based approach integrating multiple gene set analysis tools for better prioritization of biologically relevant pathways.

Gene set enrichment (GSE) analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integ...

G2Vec: Distributed gene representations for identification of cancer prognostic genes.

Identification of cancer prognostic genes is important in that it can lead to accurate outcome prediction and better therapeutic trials for cancer pat...