Colon Cancer

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervi...

The rise of robotic colorectal surgery: better for patients and better for surgeons.

Robotic colorectal surgery represents a major technological advancement in the treatment of patients with colorectal disease. Several recent randomized controlled trials comparing robotic colorectal surgery with laparoscopic surgery have demonstrated improved short-term patient outcomes in the robotic group. Whilst the primary focus of research in robotic surgery has been on patient outcomes, the ...

Assessment of the quality of online patient-oriented information on robotic colorectal surgery.

With advances in modern medicine, there is a constant need for accurate and up-to-date readily available information online to ensure patients are wel...

Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial.

BACKGROUND & AIMS: Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal...

Role of the artificial intelligence in the management of T1 colorectal cancer.

Approximately 10% of submucosal invasive (T1) colorectal cancers demonstrate extraintestinal lymph node metastasis, necessitating surgical interventio...

Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial.

INTRODUCTION: Both artificial intelligence (AI) and distal attachment devices have been shown to improve adenoma detection rate and reduce miss rate d...

Robot-assisted radical resection of colorectal cancer using the KangDuo surgical robot versus the da Vinci Xi robotic system: short-term outcomes of a multicentre randomised controlled noninferiority trial.

BACKGROUND: The KangDuo surgical robot (KD-SR-01) was recently developed in China. This study aims to evaluate the short-term outcomes of KD-SR-01 for...

Development and multicenter validation of deep convolutional neural network-based detection of colorectal cancer on abdominal CT.

OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neu...

Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films.

Through the capture of a target molecule at the metal surface with a highly confined electromagnetic field induced by surface plasmon, surface enhance...

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized wel...

Impact of AI-aided colonoscopy in clinical practice: a prospective randomised controlled trial.

OBJECTIVE: Colorectal cancer (CRC) has a significant role in cancer-related mortality. Colonoscopy, combined with adenoma removal, has proven effectiv...

Impact of study design on adenoma detection in the evaluation of artificial intelligence-aided colonoscopy: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Randomized controlled trials (RCTs) have reported that artificial intelligence (AI) improves endoscopic polyp detection. Differen...

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer.

With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagno...

A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled trial.

BACKGROUND: Up to 45.9% of polyps are missed during colonoscopy, which is the major cause of post-colonoscopy colorectal cancer (CRC). Computer-aided ...

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of...

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibit...

Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model.

Dysbiosis is associated with colorectal cancer (CRC) and adenomas (CRA). However, the robustness of diagnostic models based on microbial signatures i...

Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma.

MYC has been identified to profoundly influence a wide range of pathologic processes in cancers. However, the prognostic value of MYC-related genes in...

Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.

BACKGROUND: Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical ma...

A Framework for the Evaluation of Human Machine Interfaces of Robot-Assisted Colonoscopy.

UNLABELLED: The Human Machine Interface (HMI) of intraluminal robots has a crucial impact on the clinician's performance. It increases or decreases th...