Trends in colorectal cancer incidence according to an increase in the number of colonoscopy cases in Korea

BACKGROUND The incidence of colorectal cancer(CRC)and preinvasive CRC(e.g.,early colon cancer and advanced adenoma)is gradually increasing in several countries.AIM To evaluate the trend in incidence of CRC and preinvasive CRC according to the increase in the number of colonoscopies performed in Korea.METHODS This retrospective cohort study enrolled Korean patients from 2002 to 2020 to evaluate the incidence of CRC and preinvasive CRC,and assess the numbers of diagnostic colonoscopies and colonoscopic polypectomies.Colonoscopy-related complications by age group were also determined.RESULTS The incidence of CRC showed a rapid increase,then decreased after 2012 in the 50-75 year-age group.During the study period,the rate of incidence of preinvasive CRC increased at a similar level in patients under 50 and 50-75 years of age.Since 2009,the increase has been rapid,showing a pattern similar to the increase in colonoscopies.The rate of colonoscopic polypectomy in patients aged under 50 was similar to the rate in patients over 75 years of age after 2007.The rate of complications after colonoscopy and related deaths within 3 mo was high for those over 75 years of age.CONCLUSION The diagnosis of preinvasive CRC increased with the increase in the number of colonoscopies performed.As the risk of colonoscopy-related hospitalization and death is high in the elderly,if early lesions at risk of developing CRC are diagnosed and treated under or at the age of 75,colonoscopy-related complications can be reduced for those aged 76 years or over.

Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer

BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.