AIMC Topic: Colorectal Neoplasms

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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

PLoS medicine
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers....

Association of time to colonoscopy after a positive fecal test result and fecal hemoglobin concentration with risk of advanced colorectal neoplasia.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: We evaluated the risk of advanced colorectal neoplasia (ACRN) and colorectal cancer (CRC) according to time to colonoscopy after positive fecal immunochemical test (FIT), fecal hemoglobin concentration, and combination of both.

MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.

Medical image analysis
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectiv...

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions.

The lancet. Gastroenterology & hepatology
Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby...

Neural network analysis of Chinese herbal medicine prescriptions for patients with colorectal cancer.

Complementary therapies in medicine
Traditional Chinese Medicine (TCM) is an experiential form of medicine with a history dating back thousands of years. The present study aimed to utilize neural network analysis to examine specific prescriptions for colorectal cancer (CRC) in clinical...

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag).

PloS one
Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient information and complete blood cell counts (CBC) to identify individuals at elevated risk of colorectal c...

A reliable method for colorectal cancer prediction based on feature selection and support vector machine.

Medical & biological engineering & computing
Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the diseas...

Comparative effectiveness of human scope assistant versus robotic scope holder in laparoscopic resection for colorectal cancer.

Surgical endoscopy
BACKGROUND: Several types of robotic scope holders have been developed to date, but there are only some experimental reports or the results of small clinical cases. The Soloassist® system is a unique robotic scope holder with which the surgeon can co...

Optical classification of neoplastic colorectal polyps - a computer-assisted approach (the COACH study).

Scandinavian journal of gastroenterology
BACKGROUND AND AIMS: Clinical data suggest that the quality of optical diagnoses of colorectal polyps differs markedly among endoscopists. The aim of this study was to develop a computer program that was able to differentiate neoplastic from non-neop...