Colon Cancer

Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters.

AIMS: To develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.

Robotic colorectal surgery and ergonomics.

Improved ergonomics for the operating surgeon may be an advantage of robotic colorectal surgery. Perceived robotic ergonomic advantages in visualisation include better exposure, three-dimensional vision, surgeon camera control, and line of sight screen location. Postural advantages include seated position and freedom from the constraints of the sterile operating field. Manipulation benefits includ...

Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in Wild-Type Metastatic Colorectal Cancer.

PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastat...

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly diff...

Detection of flat colorectal neoplasia by artificial intelligence: A systematic review.

OBJECTIVES: This study review focuses on a deep learning method for the detection of colorectal lesions in colonoscopy and AI support for detecting co...

A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy.

Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has...

Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.

Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In additi...

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning.

Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge an...

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed netwo...

A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.

The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung ...

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network.

A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be remove...

Consolidated domain adaptive detection and localization framework for cross-device colonoscopic images.

Automatic polyp detection has been proven to be crucial in improving the diagnosis accuracy and reducing colorectal cancer mortality during the precan...

Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016.

IMPORTANCE: Quality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mor...

Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.

Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening tec...

A deep learning segmentation strategy that minimizes the amount of manually annotated images.

Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentati...

An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy.

Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prosta...

IL-8, MSPa, MIF, FGF-9, ANG-2 and AgRP collection were identified for the diagnosis of colorectal cancer based on the support vector machine model.

Colorectal cancer (CRC) is one of the most common cancer, and the early detection of CRC is essential to improve the survival rate of patients. To ide...

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatme...

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.

BACKGROUND: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is es...

Outcomes and complications of patients undergoing Salter's innominate osteotomies for hip dysplasia: a systematic review of comparative studies.

The purpose of this systematic review is to assess outcomes and complications of patients undergoing Salter's innominate osteotomies (SIOs) for the co...