AIMC Topic: Colorectal Neoplasms

Clear Filters Showing 441 to 450 of 794 articles

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

Journal of cancer research and clinical oncology
PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has be...

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

Journal of clinical pathology
AIMS: Microscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution deep learning network with self-attention mechanism (DRSANet) which combines context and details for ...

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

Current oncology (Toronto, Ont.)
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients' survival rate. In ...

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

The international journal of medical robotics + computer assisted surgery : MRCAS
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.

Journal of biophotonics
Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue assessment and biopsy targeting, with a high optical resolution and an imaging depth of ~1 mm. Recent...

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

PloS one
Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapi...

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care.

Contrast media & molecular imaging
Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP syste...

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

Scientific reports
Risk evaluation of lymph node metastasis (LNM) for endoscopically resected submucosal invasive (T1) colorectal cancers (CRC) is critical for determining therapeutic strategies, but interobserver variability for histologic evaluation remains a major p...

Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data.

Cells
The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empiri...

Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.

Nature communications
Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-base...