Colon Cancer

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 test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using ...

Automatic polyp frame screening using patch based combined feature and dictionary learning.

Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned problem our study aims to improve the human detection performance by developing an automatic polyp screening framework as a decision support tool. We use a ...

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 l...

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...

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...

Application of Artificial Neural Network in miRNA Biomarker Selection and Precise Diagnosis of Colorectal Cancer.

BACKGROUND: The early diagnosis of colorectal cancer (CRC) is associated with improved survival rates, and development of novel non-invasive, sensitiv...

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach.

In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epi...

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

BACKGROUND & AIMS: The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the...

Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy.

Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal can...

Is Robotic Complete Mesocolic Excision Feasible for Transverse Colon Cancer?

Laparoscopic complete mesocolic excision (CME) for transverse colon cancer is technically challenging. Robotic technology has been developed to reduc...

Plasminogen activator inhibitor-1 is associated with the metabolism and development of advanced colonic polyps.

Implications of plasminogen activator inhibitor-1 (PAI-1) in colonic polyps remain elusive. A prospective study was conducted with 188 consecutive sub...

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training.

To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to...

Designing Anticancer Peptides by Constructive Machine Learning.

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular desig...

Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates.

BACKGROUND: ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical ...

Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience.

A survey on Barrett's esophagus analysis using machine learning.

This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treat...

Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls.

Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of mini...

High preoperative serum CA 19-9 levels can predict poor oncologic outcomes in colorectal cancer patients on propensity score analysis.

PURPOSE: The purpose of this study is to evaluate the prognostic value of preoperative serum CA 19-9 levels in colorectal cancer patients.

A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.

Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained fr...

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning.

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic...