Colon Cancer

Artificial intelligence for polyp characterization: Don't save on your competence!

Artificial intelligence for colonoscopic polyp detection: High performance versus human nature.

A fatal invasive pulmonary infection in an adult patient with malignant lung adenocarcinoma.

BACKGROUND AND PURPOSE: complex as a ubiquitous environmental mold is increasingly reported to cause an invasive fungal infection in immunosuppressiv...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status ...

Colon capsule endoscopy versus CT colonography after incomplete colonoscopy. Application of artificial intelligence algorithms to identify complete colonic investigations.

BACKGROUND: Guidelines suggest computed tomography colonography (CTC) following incomplete optical colonoscopy (OC). Colon capsule endoscopies (CCE) h...

Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality.

BACKGROUND: Colonoscopy is the gold standard for polyp detection, but polyps may be missed. Artificial intelligence (AI) technologies may assist in po...

Using Computer-Aided Polyp Detection During Colonoscopy.

Computer-Aided Diagonosis for Colorectal Cancer using Deep Learning with Visual Explanations.

Detection, diagnosis, and removal of colorectal neoplasms are well-accepted colorectal cancer prevention methods. Although promising endoscopic imagin...

Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.

Artificial intelligence (AI) could change the practice of gastroenterology through its ability to both acquire and analyze information with speed, rep...

[Application of convolutional neural network to risk evaluation of positive circumferential resection margin of rectal cancer by magnetic resonance imaging].

To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection m...

Advanced Machine Learning in Prediction of Second Primary Cancer in Colorectal Cancer.

Colorectal cancer (CRC) ranked third among most commonly diagnosed cancers worldwide. The onset of second primary cancer (SPC) is an important indicat...

Artificial Intelligence Will Not Replace Health Professionals, but the Proper Use of Artificial Intelligence Will Make Health Professionals Better.

Deep learning has enabled great advances to be made in cancer research with regards to diagnosis, prognosis, and treatment. The study by Wang and coll...

Identification of prognostic immune-related signature predicting the overall survival for colorectal cancer.

OBJECTIVE: The morbidity and mortality of patients with colorectal cancer, one of the most common malignant tumors worldwide, is steadily increasing. ...

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study.

BACKGROUND: Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. ...

Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds.

OBJECTIVES: Reliable in situ diagnosis of diminutive (≤5 mm) colorectal polyps could allow for "resect and discard" and "diagnose and leave" strategie...

Artificial Intelligence for the Determination of a Management Strategy for Diminutive Colorectal Polyps: Hype, Hope, or Help.

Most colorectal polyps are diminutive, and malignant potential for these polyps is uncommon, especially for those in the rectosigmoid. However, many d...

Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy.

BACKGROUND/AIM: To study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy.

Automated sample preparation with SP3 for low-input clinical proteomics.

High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, inclu...

Diterpenoids from Roots of and Toxicity against Human Cancer Cell Lines.

Further investigations on phytochemical constituents of dichloromethane extract from roots of ( led to the isolation and identification of eight know...

Mediated Synthesis of Silver Nanoparticles and Its Evaluation of Antineoplastic Activity to Combat Colorectal Cancer Cell Line.

Several formulations of herbal plants have been extensively applied to treat diseases. ( is an Iranian traditional plant with a wide range of benefit...