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Updated: May 7, 2026

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Establishment and validation of a clinical prediction model for colorectal adenoma risk factors
Dong-Lin Li1, Ling-Ling Ma2, Zhong-An Guan3
1The First College of Clinical Medicine, Shandong Traditional Chinese Medicine University, Jinan, Shandong 250000, P.R. China.
Abstract:
Colorectal adenomas are benign tumors of the colorectal mucosal epithelium that have malignant potential and are regarded as precancerous lesions of colorectal cancer, for which the specific risk factors are unclear. The present study aimed to identify independent risk factors for colorectal adenoma to develop a prediction model and test its predictive value. A retrospective analysis was performed using data from patients who underwent electronic colonoscopy at the Department of Proctology (Affiliated Hospital of Shandong University of Traditional Chinese Medicine; Jinan, China) from January 2013 to December 2023 and had polyps removed during colonoscopy. Patients with colorectal adenoma were included in the case group, whilst those with no visible abnormalities on endoscopy or with non-adenomatous polyps were included as a control group. The patients were randomly divided into a training and validation group in a 7:3 ratio. Variables were screened using single-component analysis and the filtered variables were employed in multivariate logistic regression to create a clinical prediction model. Finally, the model was internally and externally validated. A total of 730 patients were included in the present study, with 286 assigned to the case group and 444 to the control group. After the initial screening of 39 variables, 12 continued to the next round, resulting in four potential predictors including age, daily number of bowel movements, thrombin time and the number of polyps. A prediction model was created based on these variables. Regarding internal validation, the C-index was 0.7054 [95% confidence interval (CI), 0.6596-0.7512] and the prediction probability in the calibration curve was close to the diagonal line of the calibration graph, indicating that the prediction probability of the model was reasonable. Regarding external validation, the C-index in the validation cohort was 0.6306 (95% CI, 0.5560-0.7053) and the calibration curve also demonstrated good identification capabilities. The Hosmer-Lemeshow test revealed that the model had a reasonable calibration degree, with χ2=9.7893, degree of freedom=8 and P=0.28. The receiver operating characteristic curve and decision curve analysis for the training and validation cohorts demonstrated good efficacy and an ideal application value. In conclusion, the model constructed in the present study demonstrated moderate predictive accuracy for colorectal adenoma risk, laying the groundwork for early detection of colorectal adenoma and secondary prevention of colorectal cancer.

