scholarly journals Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

2021 ◽  
Vol 27 (38) ◽  
pp. 6399-6414
Author(s):  
Michelle Viscaino ◽  
Javier Torres Bustos ◽  
Pablo Muñoz ◽  
Cecilia Auat Cheein ◽  
Fernando Auat Cheein
2010 ◽  
Vol 48 (08) ◽  
Author(s):  
A Rosenthal ◽  
H Köppen ◽  
R Musikowski ◽  
R Schwanitz ◽  
J Behrendt ◽  
...  

2020 ◽  
Vol 22 (1) ◽  
pp. 137-145
Author(s):  
Tomasz Mackiewicz ◽  
Aleksander Sowa ◽  
Jakub Fichna

: Colitis-associated colorectal cancer (CAC) remains a critical complication of ulcerative colitis (UC) with mortality of approximately 15%, which makes early CAC diagnosis crucial. The current standard of surveillance, with repetitive colonoscopies and histological testing of biopsied mucosa samples is burdensome and expensive, and therefore less invasive methods and reliable biomarkers are needed. Significant progress has been made thanks to continuous extensive research in this field, however no clinically relevant biomarker has been established so far. This review of the current literature presents the genetic and molecular differences between CAC and sporadic colorectal cancer and covers progress made in the early detection of CAC carcinogenesis. It focuses on biomarkers under development, which can be easily tested in samples of body fluids or breath and, once made clinically available, will help to differentiate between progressors (UC patients who will develop dysplasia) from non-progressors and enable early intervention to decrease the risk of cancer development.


2021 ◽  
pp. 115695
Author(s):  
Muzammil Khan ◽  
Muhammad Taqi Mehran ◽  
Zeeshan Ul Haq ◽  
Zahid Ullah ◽  
Salman Raza Naqvi

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chang Woo Kim ◽  
Hyunjin Kim ◽  
Hyoung Rae Kim ◽  
Bong-Hyeon Kye ◽  
Hyung Jin Kim ◽  
...  

Abstract Background Prevention and early detection of colorectal cancer (CRC) is a global priority, with many countries conducting population-based CRC screening programs. Although colonoscopy is the most accurate diagnostic method for early CRC detection, adherence remains low because of its invasiveness and the need for extensive bowel preparation. Non-invasive fecal occult blood tests or fecal immunochemical tests are available; however, their sensitivity is relatively low. Syndecan-2 (SDC2) is a stool-based DNA methylation marker used for early detection of CRC. Using the EarlyTect™-Colon Cancer test, the sensitivity and specificity of SDC2 methylation in stool DNA for detecting CRC were previously demonstrated to be greater than 90%. Therefore, a larger trial to validate its use for CRC screening in asymptomatic populations is now required. Methods All participants will collect their stool (at least 20 g) before undergoing screening colonoscopy. The samples will be sent to a central laboratory for analysis. Stool DNA will be isolated using a GT Stool DNA Extraction kit, according to the manufacturer’s protocol. Before performing the methylation test, stool DNA (2 µg per reaction) will be treated with bisulfite, according to manufacturer’s instructions. SDC2 and COL2A1 control reactions will be performed in a single tube. The SDC2 methylation test will be performed using an AB 7500 Fast Real-time PCR system. CT values will be calculated using the 7500 software accompanying the instrument. Results from the EarlyTect™-Colon Cancer test will be compared against those obtained from colonoscopy and any corresponding diagnostic histopathology from clinically significant biopsied or subsequently excised lesions. Based on these results, participants will be divided into three groups: CRC, polyp, and negative. The following clinical data will be recorded for the participants: sex, age, colonoscopy results, and clinical stage (for CRC cases). Discussion This trial investigates the clinical performance of a device that allows quantitative detection of a single DNA marker, SDC2 methylation, in human stool DNA in asymptomatic populations. The results of this trial are expected to be beneficial for CRC screening and may help make colonoscopy a selective procedure used only in populations with a high risk of CRC. Trial registration: This trial (NCT04304131) was registered at ClinicalTrials.gov on March 11, 2020 and is available at https://clinicaltrials.gov/ct2/show/NCT04304131?cond=NCT04304131&draw=2&rank=1.


Author(s):  
Jawad Rasheed ◽  
Akhtar Jamil ◽  
Alaa Ali Hameed ◽  
Fadi Al-Turjman ◽  
Ahmad Rasheed

2020 ◽  
Vol 9 (10) ◽  
pp. 3313 ◽  
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Aman Ali ◽  
...  

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.


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