Background and Aim Little is known regarding the exact burden of inflammatory bowel disease (IBD) in Asian countries because previous epidemiologic studies were hospital based. We aimed to develop and validate an operational definition of IBD cases from health insurance claims data and to examine the epidemiological features of IBD in Korea. Methods We analyzed stratified sample data from the Korean Health Insurance Review and Assessment (2010–2016) database using 12 different definitions and applied the best definition to the entire (2007–2016) dataset. Results The definition that combined the International Classification of Disease 10th revision code with IBD‐specific medications had the best performance characteristics among the 12 tested definitions. During the 8‐year study period, IBD prevalence increased from 25 345 in 2009 to 47 444 in 2016. Over that period, the prevalence of Crohn's disease increased 1.9‐fold (from 16.0/100 000 in 2009 to 29.6/100 000 in 2016) and that of ulcerative colitis increased 1.6‐fold (from 41.4/100 000 in 2009 to 66.0/100 000 in 2016). Similarly, the estimated incidence of Crohn's disease also increased 1.2‐fold (from 2.4 to 2.9 per 100 000) and that of ulcerative colitis rose 1.3‐fold (from 4.0 to 5.3 per 100 000). During the study period, the predominant increase in IBD incidence was among younger individuals, especially those aged < 30 years. Conclusion Patients with IBD can be accurately identified using Korean insurance claims data by combining information regarding the International Classification of Disease 10th revision codes and the IBD medications used. The prevalence of IBD continues to increase, with an apparent shift toward younger (< 30 years) age groups.

Background/Aims: Little is known about the national colonoscopy volume in Asian countries. This study aimed to assess the national colonoscopy volume in Korea over a 12year period on the basis of a nationwide population-based database. Methods: We conducted a population-based study for colonoscopy claims (14,511,158 colonoscopies performed on 13,219,781 patients) on the basis of the Korean National Health Insurance Service database from 2002 to 2013. The 12-year national colonoscopy burden was analyzed according to patient age, patient sex, and healthcare facility type. Results: The overall volume of colonoscopy increased 8-fold over the 12-year period. The annual colonoscopic polypectomy rate significantly increased in all patient sex and age groups over the 12-years period (all p<0.001). The yearly colonoscopic polypectomy rate for men was significantly increased compared with that for women (2.3% vs 1.7%, p<0.001) and for the screening-age group compared with that for the young-age group (2.0% vs 1.6%, p<0.001). The yearly colonoscopic polypectomy rate relative to the total colonoscopy volume significantly increased in primary, secondary, and tertiary facilities by 2.4%, 1.9%, and 1.4% during the 12-year period (all p<0.001). In addition, the annual colonoscopy volume covered by high-volume facilities significantly increased by 1.8% in primary healthcare facilities over the 12-year period (p<0.001). Conclusions: Healthcare resources should be prioritized to allow adequate colonoscopic capacity, especially for men, individuals in the screeningage group, and at primary healthcare facilities. Cost-effective strategies to improve the quality of colonoscopy may focus on primary healthcare facilities and high-volume facilities in

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BackgroundHuman evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), although accurate prediction is essential to determine treatment and follow-up strategies for colon cancer. We aimed to develop accurate histopathological features for LNM in colon cancer.MethodsWe developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Diseases and the pathology archive at the University Medical Center Mannheim, Germany. This model was applied to whole-slide pathological images of colon cancer patients from The Cancer Genome Atlas (TCGA). The predictive value of the peri-tumoral stroma (PTS) score for LNM was assessed.ResultsA total of 164 patients with stages I, II, and III colon cancer from TCGA were analyzed. The mean PTS score was 0.380 (± SD = 0.285), and significantly higher PTS scores were observed in patients in the LNM-positive group than those in the LNM-negative group (P &lt; 0.001). In the univariate analyses, the PTS scores for the LNM-positive group were significantly higher than those for the LNM-negative group (P &lt; 0.001). Further, the PTS scores in lymphatic invasion and any one of perineural, lymphatic, or venous invasion were significantly increased in the LNM-positive group (P &lt; 0.001 and P &lt; 0.001).ConclusionWe established the PTS score, a simplified reproducible parameter, for predicting LNM in colon cancer using computer-based analysis that could be used to guide treatment decisions. These findings warrant further confirmation through large-scale prospective clinical trials.

In this paper we study the homological properties of various natural modules associated to the Fourier algebra of a locally compact group. In particular, we focus on the question of identifying when such modules are projective in the category of operator spaces. We will show that projectivity often implies that the underlying group is discrete and give evidence to show that amenability also plays an important role.

BackgroundIdentification of a simplified prediction model for lymph node metastasis (LNM) for patients with early colorectal cancer (CRC) is urgently needed to determine treatment and follow-up strategies. Therefore, in this study, we aimed to develop an accurate predictive model for LNM in early CRC.MethodsWe analyzed data from the 2004-2016 Surveillance Epidemiology and End Results database to develop and validate prediction models for LNM. Seven models, namely, logistic regression, XGBoost, k-nearest neighbors, classification and regression trees model, support vector machines, neural network, and random forest (RF) models, were used.ResultsA total of 26,733 patients with a diagnosis of early CRC (T1) were analyzed. The models included 8 independent prognostic variables; age at diagnosis, sex, race, primary site, histologic type, tumor grade, and, tumor size. LNM was significantly more frequent in patients with larger tumors, women, younger patients, and patients with more poorly differentiated tumor. The RF model showed the best predictive performance in comparison to the other method, achieving an accuracy of 96.0%, a sensitivity of 99.7%, a specificity of 92.9%, and an area under the curve of 0.991. Tumor size is the most important features in predicting LNM in early CRC.ConclusionWe established a simplified reproducible predictive model for LNM in early CRC that could be used to guide treatment decisions. These findings warrant further confirmation in large prospective clinical trials.