Colon Cancer

Classification of Multiple H&E Images via an Ensemble Computational Scheme.

In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the hist...

Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning.

INTRODUCTION: The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population.

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract.

. The objectives of the proposed work are twofold. Firstly, to develop a specialized light weight CRPU-Net for the segmentation of polyps in colonosco...

A Cohort Study Investigating Zearalenone Concentrations and Selected Steroid Levels in Patients with Sigmoid Colorectal Cancer or Colorectal Cancer.

THE AIM: In this study was to determine if sigmoid colorectal cancer (SCC) and colorectal cancer (CRC) in women (W) and men (M) is accompanied by zear...

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. ...

AN ELECTRONIC MEDICAL RECORD PREDICTION MODEL TO IDENTIFY INADEQUATE BOWEL PREPARATION IN PATIENTS AT OUTPATIENT COLONOSCOPY.

BACKGROUND AND AIMS: Inadequate bowel preparation during colonoscopy is associated with decreased adenoma detection, increased costs, and patient proc...

Towards Semi-Autonomous Colon Screening Using an Electromagnetically Actuated Soft-Tethered Colonoscope Based on Visual Servo Control.

OBJECTIVE: Conventional colonoscopy using a flexible colonoscope remains two major limitations, including patient discomfort and difficult manipulatio...

Albumin-Bilirubin (ALBI) Score and Systemic Immune-Inflammation Indexes Used As Pretreatment Outcome Predictors in Patients With Pancreatic Ductal Adenocarcinoma Undergoing Robotic or Open Whipple Procedures: A Logistic Regression Analysis.

Background Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer (PC) in the United States. In patients with resectable...

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method.

BACKGROUND: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early...

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis.

BACKGROUND: Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of...

Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video.

BACKGROUND: The performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only...

Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy.

Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integra...

Advancement of artificial intelligence systems for surveillance endoscopy of Barrett's esophagus.

Barrett's esophagus (BE) is a precursor disease for esophageal adenocarcinoma. Timely detection and treatment has significant influence on patient out...

Secret learning for lung cancer diagnosis-a study with homomorphic encryption, texture analysis and deep learning.

Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is ...

Application of artificial intelligence in endoscopic gastrointestinal tumors.

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better...

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.

BACKGROUND: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metast...

Colonoscopy setting and impact on statistical significance in Artificial Intelligence trials.

Cancer care pathways across seven countries in Europe: What are the current obstacles? And how can artificial intelligence help?

BACKGROUND: Cancer poses significant challenges for healthcare professionals across the disease pathway including cancer imaging. This study constitut...

Noninvasive Computed Tomography-Based Deep Learning Model Predicts In Vitro Chemosensitivity Assay Results in Pancreatic Cancer.

OBJECTIVES: We aimed to predict in vitro chemosensitivity assay results from computed tomography (CT) images by applying deep learning (DL) to optimiz...

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.

SIGNIFICANCE: Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring ad...