Abstract
Colorectal cancer (CRC) is one of the most common causes of cancer mortality in the world. The incidence is related to increases with age and western dietary habits. Early detection through screening by colonoscopy has been proven to effectively reduce disease-related mortality. Currently, it is generally accepted that most colorectal cancers originate from adenomas. This is known as the “adenoma–carcinoma sequence”, and several studies have shown that early detection and removal of adenomas can effectively prevent the development of colorectal cancer. The other two pathways for CRC development are the Lynch syndrome pathway and the sessile serrated pathway. The adenoma detection rate is an established indicator of a colonoscopy’s quality. A 1% increase in the adenoma detection rate has been associated with a 3% decrease in interval CRC incidence. However, several factors may affect the adenoma detection rate during a colonoscopy, and techniques to address these factors have been thoroughly discussed in the literature. Interestingly, despite the use of these techniques in colonoscopy training programs and the introduction of quality measures in colonoscopy, the adenoma detection rate varies widely. Considering these limitations, initiatives that use deep learning, particularly convolutional neural networks (CNNs), to detect cancerous lesions and colonic polyps have been introduced. The CNN architecture seems to offer several advantages in this field, including polyp classification, detection, and segmentation, polyp tracking, and an increase in the rate of accurate diagnosis. Given the challenges in the detection of colon cancer affecting the ascending (proximal) colon, which is more common in women aged over 65 years old and is responsible for the higher mortality of these patients, one of the questions that remains to be answered is whether CNNs can help to maximize the CRC detection rate in proximal versus distal colon in relation to a gender distribution. This review discusses the current challenges facing CRC screening and training programs, quality measures in colonoscopy, and the role of CNNs in increasing the detection rate of colonic polyps and early cancerous lesions.
Highlights
Colorectal cancer (CRC) is responsible for approximately 10% of cancer-related mortality in western countries and is considered the second leading cause of death from cancers in the UnitedStates and the United Kingdom [1]
This review focuses on colonic polyps and the factors that increase their risk of becoming cancerous, the significance of screening for CRC, colonoscopy training programs, quality measures in colonoscopy, the current opinion on the role of convolutional neural networks (CNNs) in the early detection of colonic polyps and cancerous lesions, and future research directions in this field
Further studies are needed to confirm the effectiveness of a CNN-computer-aided diagnosis (CAD) system in routine colonoscopy
Summary
Colorectal cancer (CRC) is responsible for approximately 10% of cancer-related mortality in western countries and is considered the second leading cause of death from cancers in the United. Several risk factors have contributed to the progressive increase in incidence of CRC, including advancing age, western dietary habits, smoking, a sedentary lifestyle, and obesity [3]. Sessile serrated polyps are usually found in the ascending (proximal) colon These lesions are heterogeneous, yet can be differentiated from conventional adenomas. Serrated polyps can be classified into three different types: hyperplastic polyps, sessile serrated adenomas/polyps, and traditional serrated adenomas The latter two types of lesions are associated with CRC development [10]. Factors that increase their risk of being cancerous are an increase in the polyp size, advanced age, a history of smoking, a family history of cancer, and non-use of nonsteroidal anti-inflammatory drugs (NSAIDs) [11]. This review focuses on colonic polyps (adenomatous polyps) and the factors that increase their risk of becoming cancerous, the significance of screening for CRC, colonoscopy training programs, quality measures in colonoscopy, the current opinion on the role of convolutional neural networks (CNNs) in the early detection of colonic polyps and cancerous lesions, and future research directions in this field
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