Barriers to the use of tests for early detection of colorectal cancer in Chile

This study aimed to assess the use of colorectal cancer (CRC) tests for prevention and early detection, alongside exploring the associated barriers to these tests. A stratified national survey was conducted in Chile, involving 1893 respondents (with a 2.3% error margin and 95% confidence interval). Logistic and multinomial regression analyses were employed to examine variations in test utilization likelihood and barrier. We found that the key determinants for undergoing CRC tests included age, health status, possession of private health insurance, and attainment of postgraduate education. Notably, 18% and 29% of respondents covered by public and private insurance, respectively, cited personal prevention as the primary motivation for test uptake. The principal obstacle identified was lack of knowledge, mentioned by 65% of respondents, while 29% and 19% of the publicly and privately insured respectively highlighted lack of access as a barrier. The results of this study provide valuable insights into factors influencing CRC screening, aiming to inform public health policies for expanding national coverage beyond diagnosis and treatment to encompass preventive measures.

Post-polypectomy follow-up recommendations by practicing academic gastroenterologists-does number of years in practice matter?

AIM

Colorectal cancer (CRC) is the third most deadly and fourth most common cancer worldwide. Early detection, resection, and appropriate surveillance of precursor polyps result in better outcomes. Colonoscopy is a safe, accurate, and effective tool for surveillance and follow-up of premalignant polyps. Recommended surveillance intervals are based on polyp, procedural, and patient-related factors. The United States Multi-Society Task Force (MSTF) on CRC publishes guidelines with periodic updates on surveillance. We sought to evaluate adherence to post-polypectomy surveillance guidelines by academic gastroenterologists at a high-volume center.

METHODS

One-year retrospective study evaluating compliance with post-polypectomy recommendations after average risk adult screening colonoscopies. Data was collected on number and size of polyps, quality of bowel prep, initial follow-up recommendations, polyp pathology, and follow-up recommendations. Correlation with the 2012 MSTF guidelines was also evaluated. Endoscopist experience was categorized as greater or less than 10 years of practice experience. Binomial regression was used to model the association between the providers' years of experience (<10 vs. >10) and the likelihood of agreement between initial assessment and post-pathology assessment.

RESULTS

There was a greater than 85% adherence to post-polypectomy surveillance guidelines, independent of endoscopist experience.

CONCLUSION

There is a high level of adherence to post-polypectomy guidelines by practicing academic gastroenterologists independent of post-fellowship clinical experience.

Artificial Intelligence in Colorectal Polyp Detection and Characterization

BACKGROUND

Over the past 20 years, the advancement of artificial intelligence (AI) and deep learning (DL) has allowed for fast sorting and analysis of large sets of data. In the field of gastroenterology, colorectal screening procedures produces an abundance of data through video and imaging. With AI and DL, this information can be used to create systems where automatic polyp detection and characterization is possible. Convoluted Neural Networks (CNNs) have proven to be an effective way to increase polyp detection and ultimately adenoma detection rates. Different methods of polyp characterization of being hyperplastic vs. adenomatous or non-neoplastic vs. neoplastic has also been investigated showing promising results.

FINDINGS

The rate of missed polyps on colonoscopy can be as high as 25%. At the beginning of the 2000s, hand-crafted machine learning (ML) algorithms were created and trained retrospectively on colonoscopy images and videos, achieving high sensitivity, specificity, and accuracy of over 90% in many of the studies. Over time, the advancement of DL and CNNs has allowed algorithms to be trained on non-medical images and applied retrospectively to colonoscopy videos and images with similar results. Within the past few years, these algorithms have been applied in real-time colonoscopies and has shown mixed results, one showing no difference while others showing increased polyp detection.Various methods of polyp characterization have also been investigated. Through AI, DL, and CNNs polyps can be identified has hyperplastic/adenomatous or non-neoplastic/neoplastic with high sensitivity, specificity, and accuracy. One of the research areas in polyp characterization is how to capture the polyp image. This paper looks at different modalities of characterizing polyps such as magnifying narrow band imaging (NBI), endocytoscopy, laser-induced florescent spectroscopy, auto-florescent endoscopy, and white-light endoscopy.

CONCLUSIONS

Overall, much progress has been made in automatic detection and characterization of polyps in real time. Barring ethical or mass adoption setbacks, it is inevitable that AI will be involved in the field of GI, especially in colorectal polyp detection and identification.