AUTHOR=Chung Kwang Hyun , Park Min Jung , Jin Eun Hyo , Seo Ji Yeon , Song Ji Hyun , Yang Sun Young , Kim Young Sun , Yim Jeong Yoon , Lim Seon Hee , Kim Joo Sung , Chung Su Jin , Park Joo Kyung
TITLE=Risk Factors for High-Risk Adenoma on the First Lifetime Colonoscopy Using Decision Tree Method: A Cross-Sectional Study in 6,047 Asymptomatic Koreans
JOURNAL=Frontiers in Medicine
VOLUME=8
YEAR=2021
URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.719768
DOI=10.3389/fmed.2021.719768
ISSN=2296-858X
ABSTRACT=
Background/Aims: As risk of colorectal neoplasm is varied even in persons with “average-risk,” risk evaluation and tailored screening are needed. This study aimed to evaluate the risk factors of high-risk adenoma (HRA) in healthy individuals and determine the characteristics of advanced neoplasia (AN) among individual polyps.
Methods: Asymptomatic adults who underwent the first lifetime screening colonoscopy at the Seoul National University Hospital Healthcare System Gangnam Center (SNUH GC) were recruited from 2004 to 2007 as SNUH GC Cohort and were followed for 10 years. Demographic and clinical characteristics were compared between the subjects with and without AN (≥10 mm in size, villous component, and/or high-grade dysplasia and/or cancer) or HRA (AN and/or 3 or more adenomas). For individual polyps, correlations between clinical or endoscopic features and histologic grades were evaluated using a decision tree method.
Results: A total of 6,047 subjects were included and 5,621 polyps were found in 2,604 (43%) subjects. Advanced age, male sex, and current smoking status were statistically significant with regards to AN and HRA. A lower incidence of AN was observed in subjects taking aspirin. In the decision tree model, the location, shape, and size of the polyp, and sex of the subject were key predictors of the pathologic type. A weak but significant association was observed between the prediction of the final tree and the histological grouping (Kendall's tau-c = 0.142, p < 0001).
Conclusions: Advanced neoplasia and HRA can be predicted using several individual characteristics and decision tree models.