Colon Cancer

Preoperative prediction of regional lymph node metastasis of colorectal cancer based on F-FDG PET/CT and machine learning.

PURPOSE: To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on F-FDG PET/CT and radiomic features using machine-learning methods.

Deep learning detects genetic alterations in cancer histology generated by adversarial networks.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection of MSI and other biomarkers. We hypothesized that histology images synthesized by conditional generative adversarial networks (CGANs) retain informatio...

Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images.

BACKGROUND AND OBJECTIVE: Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better ...

Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.

Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. T...

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

BACKGROUND: Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples,...

Deep learning classification of lung cancer histology using CT images.

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reli...

A robust electrochemical immunosensor based on core-shell nanostructured silica-coated silver for cancer (carcinoembryonic-antigen-CEA) diagnosis.

This work addresses the fabrication of an efficient, novel, and economically viable immunosensing armamentarium that will detect the carcinoembryonic ...

A novel method to objectively assess robotic assistance in laparoscopic colorectal surgery.

OBJECTIVE: An objective assessment of robotic assistance in colorectal surgery.

State of the art in robotic rectal surgery: marginal gains worth the pain?

After their first introduction in the 1990s to overcome the limitations of conventional laparoscopic surgery, especially in confined spaces such as th...

Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms.

The treatment plan of colorectal neoplasm differs based on histology. Although new endoscopic imaging systems have been developed, there are clear dia...

Generation of Dual functional Nanobody-Nanoluciferase Fusion and its potential in Bioluminescence Enzyme Immunoassay for trace Glypican-3 in Serum.

Glypican-3 (GPC3) is a serological biomarker for the diagnosis of Hepatocellular carcinoma (HCC), but it is a challenging task to develop a bioassay f...

Artificial intelligence for colonoscopy: the new Silk Road.

Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network.

BACKGROUND: Colorectal cancer is harmful to the patient's life. The treatment of patients is determined by accurate preoperative staging. Magnetic res...

Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study.

Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine lear...

Computational learning of features for automated colonic polyp classification.

Shape, texture, and color are critical features for assessing the degree of dysplasia in colonic polyps. A comprehensive analysis of these features is...

Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed?

The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive co...

Propensity matched analysis of short term oncological and perioperative outcomes following robotic and thoracolaparoscopic esophagectomy for carcinoma esophagus- the first Indian experience.

Thoracolaparoscopic esophagectomy (TLE) for carcinoma esophagus has better short-term outcomes compared to open esophagectomy. The precise role of rob...

Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic...

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patc...

A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution.

Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automatic colorectal polyp segmentation and detection in c...