Risk Factor and Risk Prediction Modeling of Rectal Neuroendocrine Tumors

• Published in: Zhongliu fangzhi yanjiu Vol. 52; no. 7; pp. 598 - 604
• Main Authors: Xie, Liang, Liu, Chang, Li, Jianhua, Li, Jianhui, Hao, Xin, Hua, Haiyang
• Format: Journal Article
• Language: Chinese; English
• Published: Tianjin China Anti-Cancer Association 2025; Magazine House of Cancer Research on Prevention and Treatment
• Subjects: Age; Anxiety; Carcinoembryonic antigen; Cholesterol; Colon; Decision making; Fatty liver; Liver; Mental depression; Neuroendocrine tumors; nomogram; Nomograms; Prediction models; predictive model; rectal neuroendocrine tumors; Rectum; Regression analysis; Risk factors; Statistical analysis; Triglycerides; Tumors
• ISSN: 1000-8578
• DOI: 10.3971/j.issn.1000-8578.2025.24.1089

Objective　To analyze the risk factors associated with the occurrence of rectal neuroendocrine tumors (RNETs) and construct a risk prediction model. Methods　Clinical data of patients who underwent electronic colonoscopy were collected. The clinical information on patients with and without RNETs were compared, and potential risk factors for RNETs were identified. Binary logistic regression was performed to analyze the relevant risk factors and construct a risk prediction model. Results　Among 164 patients, 66 were diagnosed with RNETs, and 98 who did not have such a condition were randomly selected. Univariate logistic regression analysis revealed that age, fatty liver, anxiety and depression, total cholesterol, triglyceride levels, and carcinoembryonic antigen (CEA) were significant factors influencing the occurrence of RNETs (P<0.05). Multivariate logistic regression analysis identified age (P=0.015), anxiety and depression (P=0.031), cholesterol level (P=0.009), fatty liver (P=0.001), and CEA (P<0.001) as independent risk factors for RNETs. The participants were randomly divided into training and test sets at a 7:3 ratio. The training set was used to construct a nomogram-based risk prediction model, and the testing set was used for internal validation. The area under the curve values for the training and testing sets were 0.843 and 0.772, respectively (P>0.05). These findings indicate a good discriminative performance. The calibration curves for the training and testing sets were in good agreement with the 45° standard line, which suggests that the predicted probabilities were consistent with the actual outcomes. Decision curve analysis showed that the model provided a high net benefit within a threshold range of 0.2 to 0.7 for clinical decision making. Conclusion　Young age, fatty liver, high CEA levels, high cholesterol levels, and anxiety and depression are independent risk factors for RNETs. The nomogram model constructed based on these risk factors exhibits a strong capability to predict the occurrence of RNETs, and clinical intervention can be considered based on the predicted probability values.