Introduction
Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths globally, with increasing incidence and a trend toward younger patients. CRC develops through the adenoma-carcinoma sequence, typically over 10 to 15 years, offering a critical “golden window” for early diagnosis and intervention. Early prevention, diagnosis, and treatment are key to reducing CRC mortality, with high-quality colonoscopy playing a vital role. The adenoma detection rate (ADR), a key measure of colonoscopy quality, is influenced by factors such as withdrawal time, bowel preparation, physician experience, cecal intubation rate, and patient characteristics. This study aims to identify risk factors for colorectal adenoma and develop a risk prediction model to improve colonoscopy screening, enhancing adenoma detection rates and reducing CRC risk.
Aims & Methods
This study aims to investigate the risk factors for colorectal adenoma based on patient characteristics and factors related to the colonoscopy procedure, and to develop a risk prediction model for colorectal adenoma.
A retrospective study design was adopted, enrolling 830 patients who underwent painless colonoscopy at the outpatient clinic of the Affiliated Wuxi People's Hospital of Nanjing Medical University between January and June 2023. Data collected included patient characteristics such as gender, age, BMI, medical history, clinical symptoms, and personal history, as well as colonoscopy-related information such as the number, anatomical location, and histological type of adenomas, insertion time, withdrawal time, bowel preparation, and the physician's experience level. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for colorectal adenoma. A nomogram risk prediction model for colorectal adenoma was constructed using R language, and its predictive performance was evaluated using ROC curves, calibration curves, and decision curves. Additionally, random forest (RF), XGBoost (eXtreme Gradient Boosting), and LightGBM (Light Gradient Boosting Machine) algorithms were applied to further validate the factors associated with the adenoma detection rate (ADR).
Results
Multivariate logistic regression analysis revealed the following independent risk factors for colorectal adenoma: age (OR = 1.05, 95% CI: 1.03–1.08, p < 0.001), male gender (OR = 1.79, 95% CI: 1.32–2.41, p = 0.005), physician experience level (OR = 1.79, 95% CI: 1.20–2.68, p = 0.005), and withdrawal time (OR = 1.00, 95% CI: 1.00–1.00, p < 0.001). The ROC curve for the risk prediction model of colorectal adenoma in the modeling cohort had an area under the curve (AUC) of 0.720, indicating good discriminative ability. The calibration curve showed good agreement between the predicted and observed values, and the decision curve analysis suggested that the model has high clinical predictive value. RF, XGBoost, and LightGBM algorithms also confirmed that age, gender, physician experience, and withdrawal time are important factors influencing ADR.
Conclusion
Male, older age, higher physician experience, and longer withdrawal time are independent risk factors for the occurrence of colorectal adenoma. This study established a risk prediction nomogram model for colorectal adenoma based on easily accessible clinical indicators, which can effectively guide colonoscopy screening, improve the detection rate of colorectal adenomas, and help reduce the incidence of colorectal cancer.