Artificial intelligence in colorectal cancer screening.

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Date: Nov. 7, 2022
From: CMAJ: Canadian Medical Association Journal(Vol. 194, Issue 43)
Publisher: CMA Impact Inc.
Document Type: Article
Length: 2,544 words
Lexile Measure: 2320L

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Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in Canada and leads to death in 10% of cases. (1) In Canada, organized CRC screening usually involves a fecal immunochemical test or guaiac fecal occult blood test every 2 years for individuals aged 50-75 years. For those with a first-degree relative with a history of CRC, screening starts at a younger age and consists of colonoscopy every 5-10 years. (1)

Colonoscopy is indicated for patients with positive biochemical results, and has both diagnostic and therapeutic implications that enable stratification for further testing and evaluation. (1) Colonoscopy may lead to identification of adenomatous and sessile serrated polyps, which vary in malignant potential, and hyperplastic lesions (a type of serrated polyp not associated with a substantial risk for malignant potential). Use of endoscopic surveillance has been shown to decrease the incidence of CRC in Canada through the detection and resection of precancerous lesions. (1)

Artificial intelligence (AI) in CRC screening increases the rate of adenoma detection, decreases existing technical variation among colonoscopists (i.e., intercolonoscopist variation) and enables characterization of diminutive polyps with high accuracy for further management. (2)

How can AI be used in screening for colorectal cancer?

Broadly, AI involves the use of machine learning to infer patterns from large training data sets to make predictions on data from individual patients. (3) Two of the more prominent applications of AI for colorectal cancer screening include computer-aided detection (CADe) and computer-aided diagnosis or differentiation (CADx). Using complex models that involve layered and sequential algorithms, or convolutional neural networks, CADe is used in the detection of lesions, whereas CADx characterizes detected lesions by performing optical biopsies, obviating the need for histopathological evaluation. (2)

Optical biopsy employs properties of light to enable real-time diagnosis of tissue, previously possible only through ex vivo histological analysis. (3) This novel technique of evaluating human tissue in vivo encompasses several different methods, including types of virtual chromoendoscopy or image-enhanced colonoscopy (e.g., narrow-band imaging), or high-magnification techniques (e.g., confocal laser endomicroscopy, endocytoscopy). The techniques use backscattering of near-infrared light to approximate tissue penetration and depth of mucosal invasion, similar to that of histological evaluation. (3) The use of CADe and CADx systems still necessitates fundamental colonoscopic techniques, including 360[degrees] inspection, appropriate suction of fluid and debris, and sufficient insufflation of the colonic lumen.

What problems could be addressed by CADe and CADx?

Strategies to improve detection of polyps during colonoscopy include optimizing bowel preparation, abiding by suggested minimum times for scope withdrawal from the cecum, using caps on the end of the scope to improve visualization and using high-definition scopes. (4,5) Despite these techniques, the rate of polyp detection and subsequent resection of precancerous lesions is largely dependent on the operator, with studies reporting a wide range in adenoma detection rate, from 7%-53% among different endoscopists. (2) If some endoscopists miss adenomas, patients are at risk of interval development of CRC. (4)

Every 1% increase in adenoma detection rate is associated with a 3% decrease in colon...

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Gale Document Number: GALE|A725096682