Colon Cancer

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.

PURPOSE: To identify the role of radiomics texture features both within and outside the nodule in predicting time to progression (TTP) and overall survival (OS) as well as response to chemotherapy in patients with non-small cell lung cancer (NSCLC).

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an important role in performing a specific treatment. Here, we developed a framework that combines k-means clustering, t-test, sensitivity analysis, self...

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populatio...

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is...

Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer.

PURPOSE: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introductio...

Artificial intelligence and colonoscopy: Current status and future perspectives.

BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endos...

Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis.

Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers fro...

Potential roles of artificial intelligence learning and faecal immunochemical testing for prioritisation of colonoscopy in anaemia.

Iron deficiency anaemia (IDA) is the most common cause of anaemia and a frequent indication for colonoscopy, although the prevalence of colorectal can...

A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma.

The purpose of the study was to compare the texture based discriminative performances between non-contrast enhanced computed tomography (NECT) and con...

Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network.

The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a n...

Synthesis of chitosan nanoparticles, chitosan-bulk, chitosan nanoparticles conjugated with glutaraldehyde with strong anti-cancer proliferative capabilities.

In recent years, natural and synthetic polymers have attracted much attention due to their great potentials in medical science. In the present study, ...

The oncological and surgical safety of robot-assisted surgery in colorectal cancer: outcomes of a longitudinal prospective cohort study.

BACKGROUND: Colorectal cancer is one of the most common cancers worldwide. Laparoscopic colorectal surgery (LCRS) is a frequently used modality. A new...

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain...

Early esophageal adenocarcinoma detection using deep learning methods.

PURPOSE: This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically i...

Anti-proliferative Effect of a Novel Anti-oxidative Peptide in Hanwoo Beef on Human Colorectal Carcinoma Cells.

The present study aimed to characterise anti-oxidant peptides from water-soluble protein extracts of Hanwoo beef and evaluate their anti-proliferative...

Higher plasma concentration of TP53-induced glycolysis and apoptosis regulator is associated with a lower risk of colorectal cancer metastasis.

PURPOSE: We aimed to explore the association of plasma TP53-induced glycolysis and apoptosis regulator (TIGAR) level with colorectal cancer (CRC) meta...

Association of time to colonoscopy after a positive fecal test result and fecal hemoglobin concentration with risk of advanced colorectal neoplasia.

BACKGROUND: We evaluated the risk of advanced colorectal neoplasia (ACRN) and colorectal cancer (CRC) according to time to colonoscopy after positive ...

MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.

The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the imp...

Colorimetric Analysis of Carcinoembryonic Antigen Using Highly Catalytic Gold Nanoparticles-Decorated MoS Nanocomposites.

Sensitive detection of carcinoembryonic antigen (CEA) is very important for early detection and cancer therapy monitoring. We utilized the highly cata...

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions.

Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, ...