Colon Cancer

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we...

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.

Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopic (FT-IR) imaging provides an alternate view of tissue in which spatially specific molecular information from unstained tissue can be utilized. Here, ...

The robotic learning curve for a newly appointed colorectal surgeon.

Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years hav...

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chem...

Automated quality assessment of large digitised histology cohorts by artificial intelligence.

Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohor...

Challenges in artificial intelligence for polyp detection.

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy.

Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithm...

Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis.

AIM: As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identif...

Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images.

AIMS: Microscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution d...

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment res...

Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis.

BACKGROUND: Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary punctur...

Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation.

Segmentation is a fundamental task in biomedical image analysis. Unlike the existing region-based dense pixel classification methods or boundary-based...

Comparison of robot-assisted and conventional laparoscopy for colorectal surgery for endometriosis: A prospective cohort study.

BACKGROUND: Our objective was to evaluate surgical outcomes of robotic compared to conventional laparoscopy for colorectal surgery for endometriosis.

Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning.

Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue a...

Machine learning predicts cancer subtypes and progression from blood immune signatures.

Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune syste...

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care.

Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis...

Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.

BACKGROUND: We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (...

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging...

Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.

Risk evaluation of lymph node metastasis (LNM) for endoscopically resected submucosal invasive (T1) colorectal cancers (CRC) is critical for determini...

Da Vinci SP robotic approach to colorectal surgery: two specific indications and short-term results.

BACKGROUND: Da Vinci® Single Port (dvSP) was recently developed. Its application in colorectal surgery is under investigation. The aim of this study w...